Constructing conclusive answers for autonomous agents

ABSTRACT

Techniques are described herein for enabling autonomous agents to generate conclusive answers. An example of a conclusive answer is text that addresses concerns of a user who is interacting with an autonomous agent. For example, an autonomous agent interacts with a user device, answering user utterances, for example questions or concerns. Based on the interactions, the autonomous agent determines that a conclusive answer is appropriate. The autonomous agent formulates the conclusive answer, which addresses multiple user utterances. The conclusive answer provided to the user device.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/654,258, filed Oct. 16, 2019, which claims priority to U.S.Provisional Application No. 62/746,261, filed Oct. 16, 2018, both ofwhich are incorporated by reference in their entirety.

TECHNICAL FIELD

This disclosure is generally concerned with linguistics. Morespecifically, within an interactive session between an autonomous agentand a user, certain embodiments formulate and provide a conclusiveanswer.

BACKGROUND

Linguistics is the scientific study of language. One aspect oflinguistics is the application of computer science to human naturallanguages such as English. Due to the greatly increased speed ofprocessors and capacity of memory, computer applications of linguisticsare on the rise. For example, computer-enabled analysis of languagediscourse facilitates numerous applications such as automated agentsthat can answer questions from users. The use autonomous agents toanswer questions, facilitate discussion, manage dialogues, and providesocial promotion is increasingly popular.

Following an interactive session with an autonomous agent, acomprehensive, conclusive answer of the session may be desired. Butexisting solutions are unable to create such an answer. In contrast,these solutions are only able to provide a short reply of text that isderived from a traditional search index. These texts fail to becomprehensive.

As such, solutions are needed that can automatically generate acomprehensive answer for use in a session with an autonomous agent.

SUMMARY

Techniques are described herein for enabling autonomous agents togenerate conclusive answers. An example of a conclusive answer is textthat addresses concerns of a user who is interacting with an autonomousagent. For example, an autonomous agent interacts with a user device,answering user utterances, for example questions or concerns. Based onthe interactions, the autonomous agent determines that a conclusiveanswer is appropriate. The autonomous agent formulates the conclusiveanswer, which addresses multiple user utterances. The conclusive answerprovided to the user device.

In an example, a method of computationally fortifying an answer usingsyntactic parse trees is provided. The method includes accessing a seedsentence including a first set of text fragments. The method furtherincludes syntactically parsing the seed sentence to generate a firstsyntactic parse tree. The method further includes obtaining a searchresult by providing, to a search engine, at least one of the first setof text fragments, wherein the search result includes a second textfragment. The method further includes syntactically parsing the searchresult to generate a second syntactic parse tree. The method furtherincludes calculating, a relevancy metric for the search result bycomputing a maximum common subgraph between the first syntactic parsetree and the second syntactic parse tree. The method further includesidentifying the search result as an additional fragment based on adetermination that the relevancy metric is greater than a firstthreshold. The method further includes constructing a paragraph from theadditional fragment. The method further includes providing the paragraphto a user device.

In another example, a method of computationally fortifying an answerusing syntactic parse trees is provided. The method includes accessing afirst seed sentence including text fragments and a second seed sentenceincluding text fragments. The method includes generating, from the firstseed sentence, a first syntactic parse tree. The method includesgenerating, from the second seed sentence, a second syntactic parsetree. The method includes identifying, from the first syntactic parsetree, a first entity. The method includes identifying, from the secondsyntactic parse tree, the first entity and a second entity. The methodincludes obtaining search results by providing the second entity to asearch engine. The method includes generating, from the search result, asecond syntactic parse tree. The method includes calculating a relevancymetric for the search result by computing a maximum common subgraphbetween the second syntactic parse tree and the second syntactic parsetree. The method includes identifying the search result as an additionalfragment based on (i) a determination that the relevancy metric isgreater than a first threshold. The method includes constructing aparagraph from fragments of the first seed sentence, fragments of thesecond seed sentence and additional fragment. The method includesproviding the paragraph to a user device.

In yet another example, a system includes a computer-readable mediumstoring non-transitory computer-executable program instructions; and aprocessing device communicatively coupled to the computer-readablemedium for executing the non-transitory computer-executable programinstructions. Executing the non-transitory computer-executable programinstructions configures the processing device to perform operations. Theoperations include accessing a seed sentence including a first set oftext fragments. The operations include syntactically parsing the seedsentence to generate a first syntactic parse tree. The operationsinclude obtaining a search result by providing at least one of the firstset of text fragments to a search engine, wherein the search resultincludes a second text fragment. The operations include syntacticallyparsing the search result to generate a second syntactic parse tree. Theoperations include calculating, a relevancy metric for the search resultby computing a maximum common subgraph between the first syntactic parsetree and the second syntactic parse tree. The operations includeidentifying the search result as an additional fragment based on adetermination that the relevancy metric is greater than a firstthreshold. The operations include constructing a paragraph from theadditional fragment; and providing the paragraph to a user device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary conclusive answer generating environment, inaccordance with an aspect of the present disclosure.

FIG. 2 depicts an example of a discourse tree in accordance with anaspect.

FIG. 3 depicts a further example of a discourse tree in accordance withan aspect.

FIG. 4 depicts illustrative schemas, in accordance with an aspect.

FIG. 5 depicts a node-link representation of the hierarchical binarytree in accordance with an aspect.

FIG. 6 depicts an exemplary indented text encoding of the representationin FIG. 5 in accordance with an aspect.

FIG. 7 depicts an exemplary DT for an example request about property taxin accordance with an aspect.

FIG. 8 depicts an exemplary response for the question represented inFIG. 7 .

FIG. 9 illustrates a discourse tree for an official answer in accordancewith an aspect.

FIG. 10 illustrates a discourse tree for a raw answer in accordance withan aspect.

FIG. 11 illustrates a communicative discourse tree for a claim of afirst agent in accordance with an aspect.

FIG. 12 illustrates a communicative discourse tree for a claim of asecond agent in accordance with an aspect.

FIG. 13 illustrates a communicative discourse tree for a claim of athird agent in accordance with an aspect.

FIG. 14 illustrates parse thickets in accordance with an aspect.

FIG. 15 illustrates an exemplary process for building a communicativediscourse tree in accordance with an aspect.

FIG. 16 illustrates an exemplary process for determining an appropriatetype of answer to be provided by an autonomous agent, in accordance withan aspect.

FIG. 17 illustrates an example of a conversation flow with a factoidanswer, in accordance with an aspect.

FIG. 18 illustrates an example of a conversation flow with a conclusiveanswer, in accordance with an aspect.

FIG. 19 illustrates an exemplary process for converting textual contentfor use in forming a conclusive answer, in accordance with an aspect.

FIG. 20 illustrates an exemplary process for building a conclusiveanswer, in accordance with an aspect.

FIG. 21 depicts a simplified diagram of a distributed system forimplementing one of the aspects

FIG. 22 is a simplified block diagram of components of a systemenvironment by which services provided by the components of an aspectsystem may be offered as cloud services in accordance with an aspect.

FIG. 23 illustrates an exemplary computer system, in which variousaspects of the present invention may be implemented.

DETAILED DESCRIPTION

Aspects disclosed herein provide technical improvements to the area ofcomputer-implemented linguistics, autonomous agents (chatbots),identifying a need for and generating conclusive answers. A conclusiveanswer is text that addresses concerns of a user who is interacting withan autonomous agent. In an example, a user device is interacting with anautonomous agent in an interactive session. During the session, the useridentifies one or more issues. The autonomous agent determines that aconclusive answer should be provided. The agent determines components tobe included in the conclusive answer, identifies and formats text thataddresses is responsive to the necessary components, and generates theconclusive answer. The conclusive answer can in turn be provided to adisplay such as a display of a mobile device.

Generating a conclusive answer can involve determining a suitablestructure of the conclusive answer and creating the conclusive answerbased on the determined structure. The structure of the conclusiveanswer can involve identifying one or more entities present in aninitial user utterance and/or subsequent user utterances. Morespecifically, certain aspects obtain content from a text corpus, verifythat the obtained text is relevant, and verify that the obtained text isappropriate (e.g., in style).

Different technical approaches can be used. For example, machinelearning, keyword-based approaches, and/or search engineering techniquescan be used. Additionally or alternatively, some aspects representrhetorical relationships between one or more sentences in communicativediscourse trees. “Communicative discourse trees” or “CDTs” includediscourse trees that are supplemented with communicative actions.Communicative discourse trees combine rhetoric information withcommunicative actions. A communicative action is a cooperative actionundertaken by individuals based on mutual deliberation andargumentation. By incorporating labels that identify communicativeactions, learning of communicative discourse trees can occur over aricher features set than simply rhetoric relations and syntax ofelementary discourse units (EDUs). With such a feature set, additionaltechniques such as classification can be used to determine a level ofrhetoric agreement between questions and answers or request-responsepairs, thereby enabling improved automated agents. Representing text ascommunicative discourse trees can facilitate different analysis such asdetection of argumentation and verification that two sentences are of asimilar style.

Technical advantages of some aspects include improved autonomous agentsand improved search engine performance over traditionalstatistical-based approaches. As discussed, existing solutions areunable to create conclusive answer. Instead, such solutions provideshort replies by text indexed by a traditional search index. Statisticallearning and especially deep learning-based agents attempt to uselearning to tailor answers to a user session, but only brief texts canbe obtained as a result. These texts can be meaningful but fail to becomprehensive.

Certain Definitions

As used herein, an “entity” has an independent and distinct existence.Examples includes objects, places, and persons. An entity can also be asubject or topic such as “electric cars” or “brakes.”

As used herein, a named entity is an entity that is identified by aname. Examples of named entities are “France,” “John Doe,” and “RussellSquare” are named entities

As used herein, “rhetorical structure theory” is an area of research andstudy that provided a theoretical basis upon which the coherence of adiscourse could be analyzed.

As used herein, “discourse tree” or “DT” refers to a structure thatrepresents the rhetorical relations for a sentence of part of asentence.

As used herein, a “rhetorical relation,” “rhetorical relationship,” or“coherence relation” or “discourse relation” refers to how two segmentsof discourse are logically connected to one another. Examples ofrhetorical relations include elaboration, contrast, and attribution.

As used herein, a “sentence fragment,” or “fragment” is a part of asentence that can be divided from the rest of the sentence. A fragmentis an elementary discourse unit. For example, for the sentence “Dutchaccident investigators say that evidence points to pro-Russian rebels asbeing responsible for shooting down the plane,” two fragments are “Dutchaccident investigators say that evidence points to pro-Russian rebels”and “as being responsible for shooting down the plane.” A fragment can,but need not, include a verb.

As used herein, “signature” or “frame” refers to a property of a verb ina fragment. Each signature can include one or more thematic roles. Forexample, for the fragment “Dutch accident investigators say thatevidence points to pro-Russian rebels,” the verb is “say” and thesignature of this particular use of the verb “say” could be “agent verbtopic” where “investigators” is the agent and “evidence” is the topic.

As used herein, “thematic role” refers to components of a signature usedto describe a role of one or more words. Continuing the previousexample, “agent” and “topic” are thematic roles.

As used herein, “nuclearity” refers to which text segment, fragment, orspan, is more central to a writer's purpose. The nucleus is the morecentral span, and the satellite is the less central one.

As used herein, “coherency” refers to the linking together of tworhetorical relations.

As used herein, “communicative verb” is a verb that indicatescommunication. For example, the verb “deny” is a communicative verb.

As used herein, “communicative action” describes an action performed byone or more agents and the subjects of the agents.

Turning now to the Figures, FIG. 1 shows an exemplary conclusive answergenerating environment, in accordance with an aspect of the presentdisclosure. FIG. 1 depicts computing device 101, data network 104,server 160, and user device 170. In the example depicted by FIG. 1 ,user device 170 interacts with computing device 101 over data network104. As depicted by FIG. 1 , a user requested a recommendation aboutinvestment, received it and expressed her doubts. The chatbot providesthe comprehensive conclusive answer entitled ‘Why a 61% revenue growthis not enough for Alibaba’ with detailed information on competitivenessincluding the charts.

User device 170 can be any mobile device such as a mobile phone, smartphone, tablet, laptop, smart watch, and the like. User device 170communicates via data network 104 to server 160 and/or computing device101. Data network 104 can be any public or private network, wired orwireless network, Wide Area Network, Local Area Network, or theInternet.

In an example, user device 170 can provide questions, e.g., questions180 and 180, which can be from a user, to computing device 101.Computing device 101 can determine a suitable answer, e.g., answer 181which is responsive to question 180, or conclusive answer 183, andprovide the answer to user device 170. In particular, computing device101 can determine that a conclusive answer is appropriate, generate theconclusive answer, and transmit the conclusive answer to user device170.

As depicted, user device 170 includes a display 172. The display 172depicts a chat history, or an interactive session with the autonomousagent implemented by computing device 101. As can be seen, a userinitially asks a question 180, which reads “I'm curious if I shouldinvest in Alibaba only or diversify into FANG (Facebook, Amazon, Google,Netflix.” In response, computing device 101 determines a suitable answer181, which reads “Consider Alibaba only because it is diversifyingitself into other areas beyond buying and selling stuff.” Computingdevice 101 then receives user question 182, which reads “Is itsuccessful in going into these other areas? Can it really compete withFANG?” In response, computing device 101 generates conclusive answer183, which reads “Alibaba reported a 61% revenue growth for the quarterending June 30. That growth rate was higher than all the peers in theBAT (Baldu, Tencent) and FANG (Facebook, Amazon, Netflix, and Google). .. . Alibaba isn't just about selling and buying goods anymore. Much likewhat Amazon is doing in the U.S., the e-commerce giant has expanded intomany other areas. It owns the streaming website Youku (China's YouTube),local services perform Koubel (similar to Yelo) and food deliveryplatform Ele.me (like Seamless).” As can be seen, conclusive answer 183addresses both question 180 and question 182, because conclusive answer183 addresses a main entity in question 180 “Alibaba” and a specificrequest about Alibaba's success as present in question 182.

Computing device 101 implements an autonomous agent via conclusiveanswer application 102, text corpus 105, rhetoric agreement classifier120, and training data 125. Conclusive answer application 102 canimplement any of the functionality described herein, including analyzingtext and questions, determining answers, and determining conclusiveanswers. Text corpus 105 includes text such as electronic documents,webpages, customer support conversation logs, internal issue resolutionlogs, or other correspondence. Conclusive answer application 102 canindex the content of text corpus 105 for quicker access. Rhetoricagreement classifier 120 can include one or more machine learning modelsthat are trained to perform one or more different actions. For example,rhetoric agreement classifier 120 can determine a level of rhetoricagreement and/or style between a question and a candidate answer. Inthis manner, conclusive answer application 102 can provide the mostsuitable answer to the mobile device 160.

Server 160 can be a public or private internet server, such as a publicdatabase of user questions and answers. Server 160 can implement any ofthe functionality described herein, including the functionalityassociated with computing device 101. In some cases, server 160 cancomplement the functionality of computing device 101, for example, byproviding additional databases, corpuses of text, etc. Examples ofadditional functionality that can be performed by computing device 101and/or server 160 are discussed with respect to FIGS. 15, 16, 19, and 20. For example, computing device 101 can perform process 1600 describedwith respect to FIG. 16 to determine whether a conclusive answer isnecessary. If a conclusive answer is necessary, then computing device101 can perform process 2000 described with respect to FIG. 20 .

Rhetoric Structure Theory and Discourse Trees

Linguistics is the scientific study of language. For example,linguistics can include the structure of a sentence (syntax), e.g.,subject-verb-object, the meaning of a sentence (semantics), e.g. dogbites man vs. man bites dog, and what speakers do in conversation, i.e.,discourse analysis or the analysis of language beyond the sentence.

The theoretical underpinnings of discourse, Rhetoric Structure Theory(RST), can be attributed to Mann, William and Thompson, Sandra,“Rhetorical structure theory: A Theory of Text organization,”Text-Interdisciplinary Journal for the Study of Discourse, 8(3):243-281,1988. Similar to how the syntax and semantics of programming languagetheory helped enable modern software compilers, RST helped enabled theanalysis of discourse. More specifically RST posits structural blocks onat least two levels, a first level such as nuclearity and rhetoricalrelations, and a second level of structures or schemas. Discourseparsers or other computer software can parse text into a discourse tree.

Rhetoric Structure Theory models logical organization of text, astructure employed by a writer, relying on relations between parts oftext. RST simulates text coherence by forming a hierarchical, connectedstructure of texts via discourse trees. Rhetoric relations are splitinto the classes of coordinate and subordinate; these relations holdacross two or more text spans and therefore implement coherence. Thesetext spans are called elementary discourse units (EDUs). Clauses in asentence and sentences in a text are logically connected by the author.The meaning of a given sentence is related to that of the previous andthe following sentences. This logical relation between clauses is calledthe coherence structure of the text. RST is one of the most populartheories of discourse, being based on a tree-like discourse structure,discourse trees (DTs). The leaves of a DT correspond to EDUs, thecontiguous atomic text spans. Adjacent EDUs are connected by coherencerelations (e.g., Attribution, Sequence), forming higher-level discourseunits. These units are then also subject to this relation linking. EDUslinked by a relation are then differentiated based on their relativeimportance: nuclei are the core parts of the relation, while satellitesare peripheral ones. As discussed, in order to determine accuraterequest-response pairs, both topic and rhetorical agreement areanalyzed. When a speaker answers a question, such as a phrase or asentence, the speaker's answer should address the topic of thisquestion. In the case of an implicit formulation of a question, via aseed text of a message, an appropriate answer is expected not onlymaintain a topic, but also match the generalized epistemic state of thisseed.

Rhetoric Relations

As discussed, aspects described herein use communicative discoursetrees. Rhetorical relations can be described in different ways. Forexample, Mann and Thompson describe twenty-three possible relations. C.Mann, William & Thompson, Sandra. (1987) (“Mann and Thompson”).Rhetorical Structure Theory: A Theory of Text Organization. Othernumbers of relations are possible.

Relation Name Nucleus Satellite Antithesis ideas favored by the ideasdisfavored by the author author Background text whose text forfacilitating understanding is being understanding facilitatedCircumstance text expressing the an interpretive context of events orideas occurring situation or time in the interpretive context Concessionsituation affirmed by situation which is author apparently inconsistentbut also affirmed by author Condition action or situation conditioningsituation whose occurrence results from the occurrence of theconditioning situation Elaboration basic information additionalinformation Enablement an action information intended to aid the readerin performing an action Evaluation a situation an evaluative commentabout the situation Evidence a claim information intended to increasethe reader's belief in the claim Interpretation a situation aninterpretation of the situation Justify text information supporting thewriter's right to express the text Motivation an action informationintended to increase the reader's desire to perform the action Non- asituation another situation which volitional causes that one, but notCause by anyone's deliberate action Non- a situation another situationwhich is volitional caused by that one, but not Result by anyone'sdeliberate action Otherwise action or situation conditioning situation(anti whose occurrence results conditional) from the lack of occurrenceof the conditioning situation Purpose an intended situation the intentbehind the situation Restatement a situation a reexpression of thesituation Solutionhood a situation or method a question, request,problem, supporting full or partial or other expressed need satisfactionof the need Summary text a short summary of that text Volitional asituation another situation which causes Cause that one, by someone'sdeliberate action Volitional a situation another situation which isResult caused by that one, by someone's deliberate action

Some empirical studies postulate that the majority of text is structuredusing nucleus-satellite relations. See Mann and Thompson. But otherrelations do not carry a definite selection of a nucleus. Examples ofsuch relations are shown below.

Relation Name Span Other Span Contrast One alternate The other alternateJoint (unconstrained) (unconstrained) List An item A next item SequenceAn item A next item

FIG. 2 depicts an example of a discourse tree, in accordance with anaspect. FIG. 2 includes discourse tree 200. Discourse tree includes textspan 201, text span 202, text span 203, relation 210 and relation 228.The numbers in FIG. 2 correspond to the three text spans. FIG. 3corresponds to the following example text with three text spans numbered1, 2, 3:

1. Honolulu, Hi. will be site of the 2017 Conference on Hawaiian History

2. It is expected that 200 historians from the U.S. and Asia will attend

3. The conference will be concerned with how the Polynesians sailed toHawaii

For example, relation 210, or elaboration, describes the relationshipbetween text span 201 and text span 202. Relation 228 depicts therelationship, elaboration, between text span 203 and 204. As depicted,text spans 202 and 203 elaborate further on text span 201. In the aboveexample, given a goal of notifying readers of a conference, text span 1is the nucleus. Text spans 2 and 3 provide more detail about theconference. In FIG. 2 , a horizontal number, e.g., 1-3, 1, 2, 3 covers aspan of text (possibly made up of further spans); a vertical linesignals the nucleus or nuclei; and a curve represents a rhetoricrelation (elaboration) and the direction of the arrow points from thesatellite to the nucleus. If the text span only functions as a satelliteand not as a nuclei, then deleting the satellite would still leave acoherent text. If from FIG. 2 one deletes the nucleus, then text spans 2and 3 are difficult to understand.

FIG. 3 depicts a further example of a discourse tree in accordance withan aspect. FIG. 3 includes components 301 and 302, text spans 305-307,relation 310 and relation 328. Relation 310 depicts the relationship310, enablement, between components 306 and 305, and 307, and 305. FIG.3 refers to the following text spans:

1. The new Tech Report abstracts are now in the journal area of thelibrary near the abridged dictionary.

2. Please sign your name by any means that you would be interested inseeing.

3. Last day for sign-ups is 31 May.

As can be seen, relation 328 depicts the relationship between entity 307and 306, which is enablement. FIG. 3 illustrates that while nuclei canbe nested, there exists only one most nuclear text span.

Constructing a Discourse Tree

Discourse trees can be generated using different methods. A simpleexample of a method to construct a DT bottom up is:

(1) Divide the discourse text into units by:

-   -   (a) Unit size may vary, depending on the goals of the analysis    -   (b) Typically, units are clauses

(2) Examine each unit, and its neighbors. Is there a relation holdingbetween them?

(3) If yes, then mark that relation.

(4) If not, the unit might be at the boundary of a higher-levelrelation. Look at relations holding between larger units (spans).

(5) Continue until all the units in the text are accounted for.

Mann and Thompson also describe the second level of building blockstructures called schemas applications. In RST, rhetoric relations arenot mapped directly onto texts; they are fitted onto structures calledschema applications, and these in turn are fitted to text. Schemaapplications are derived from simpler structures called schemas (asshown by FIG. 4 ). Each schema indicates how a particular unit of textis decomposed into other smaller text units. A rhetorical structure treeor DT is a hierarchical system of schema applications. A schemaapplication links a number of consecutive text spans, and creates acomplex text span, which can in turn be linked by a higher-level schemaapplication. RST asserts that the structure of every coherent discoursecan be described by a single rhetorical structure tree, whose top schemacreates a span encompassing the whole discourse.

FIG. 4 depicts illustrative schemas, in accordance with an aspect. FIG.4 shows a joint schema is a list of items consisting of nuclei with nosatellites. FIG. 4 depicts schemas 401-406. Schema 401 depicts acircumstance relation between text spans 410 and 428. Scheme 402 depictsa sequence relation between text spans 420 and 421 and a sequencerelation between text spans 421 and 422. Schema 403 depicts a contrastrelation between text spans 430 and 431. Schema 404 depicts a jointrelationship between text spans 440 and 441. Schema 405 depicts amotivation relationship between 450 and 451, and an enablementrelationship between 452 and 451. Schema 406 depicts joint relationshipbetween text spans 460 and 462. An example of a joint scheme is shown inFIG. 4 for the three text spans below:

1. Skies will be partly sunny in the New York metropolitan area today.

2. It will be more humid, with temperatures in the middle 80's.

3. Tonight will be mostly cloudy, with the low temperature between 65and 70.

While FIGS. 2-4 depict some graphical representations of a discoursetree, other representations are possible.

FIG. 5 depicts a node-link representation of the hierarchical binarytree in accordance with an aspect. As can be seen from FIG. 5 , theleaves of a DT correspond to contiguous non-overlapping text spanscalled Elementary Discourse Units (EDUs). Adjacent EDUs are connected byrelations (e.g., elaboration, attribution . . . ) and form largerdiscourse units, which are also connected by relations. “Discourseanalysis in RST involves two sub-tasks: discourse segmentation is thetask of identifying the EDUs, and discourse parsing is the task oflinking the discourse units into a labeled tree.” See Joty, Shafiq R andGiuseppe Carenini, Raymond T Ng, and Yashar Mehdad. 2013. Combiningintra-and multi-sentential rhetorical parsing for document-leveldiscourse analysis. In ACL (1), pages 486-496.

FIG. 5 depicts text spans that are leaves, or terminal nodes, on thetree, each numbered in the order they appear in the full text, shown inFIG. 6 . FIG. 5 includes tree 500. Tree 500 includes, for example, nodes501-507. The nodes indicate relationships. Nodes are non-terminal, suchas node 501, or terminal, such as nodes 502-507. As can be seen, nodes503 and 504 are related by a joint relationship. Nodes 502, 505, 506,and 508 are nuclei. The dotted lines indicate that the branch or textspan is a satellite. The relations are nodes in gray boxes.

FIG. 6 depicts an exemplary indented text encoding of the representationin FIG. 5 in accordance with an aspect. FIG. 6 includes text 600 andtext sequences 602-604. Text 600 is presented in a manner more amenableto computer programming. Text sequence 602 corresponds to node 502,sequence 603 corresponds to node 503, and sequence 604 corresponds tonode 504. In FIG. 6 , “N” indicates a nucleus and “S” indicates asatellite.

Examples of Discourse Parsers

Automatic discourse segmentation can be performed with differentmethods. For example, given a sentence, a segmentation model identifiesthe boundaries of the composite elementary discourse units by predictingwhether a boundary should be inserted before each particular token inthe sentence. For example, one framework considers each token in thesentence sequentially and independently. In this framework, thesegmentation model scans the sentence token by token, and uses a binaryclassifier, such as a support vector machine or logistic regression, topredict whether it is appropriate to insert a boundary before the tokenbeing examined. In another example, the task is a sequential labelingproblem. Once text is segmented into elementary discourse units,sentence-level discourse parsing can be performed to construct thediscourse tree. Machine learning techniques can be used.

In one aspect of the present invention, two Rhetorical Structure Theory(RST) discourse parsers are used: CoreNLPProcessor which relies onconstituent syntax, and FastNLPProcessor which uses dependency syntax.See Surdeanu, Mihai & Hicks, Thomas & Antonio Valenzuela-Escarcega,Marco. Two Practical Rhetorical Structure Theory Parsers. (2015).

In addition, the above two discourse parsers, i.e., CoreNLPProcessor andFastNLPProcessor use Natural Language Processing (NLP) for syntacticparsing. For example, the Stanford CoreNLP gives the base forms ofwords, their parts of speech, whether they are names of companies,people, etc., normalize dates, times, and numeric quantities, mark upthe structure of sentences in terms of phrases and syntacticdependencies, indicate which noun phrases refer to the same entities.Practically, RST is a still theory that may work in many cases ofdiscourse, but in some cases, it may not work. There are many variablesincluding, but not limited to, what EDU's are in a coherent text, i.e.,what discourse segmenters are used, what relations inventory is used andwhat relations are selected for the EDUs, the corpus of documents usedfor training and testing, and even what parsers are used. So forexample, in Surdeanu, et al., “Two Practical Rhetorical Structure TheoryParsers,” paper cited above, tests must be run on a particular corpususing specialized metrics to determine which parser gives betterperformance. Thus unlike computer language parsers which givepredictable results, discourse parsers (and segmenters) can giveunpredictable results depending on the training and/or test text corpus.Thus, discourse trees are a mixture of the predicable arts (e.g.,compilers) and the unpredictable arts (e.g., like chemistry wereexperimentation is needed to determine what combinations will give youthe desired results).

In order to objectively determine how good a Discourse analysis is, aseries of metrics are being used, e.g., Precision/Recall/F1 metrics fromDaniel Marcu, “The Theory and Practice of Discourse Parsing andSummarization,” MIT Press, (2000). Precision, or positive predictivevalue is the fraction of relevant instances among the retrievedinstances, while recall (also known as sensitivity) is the fraction ofrelevant instances that have been retrieved over the total amount ofrelevant instances. Both precision and recall are therefore based on anunderstanding and measure of relevance. Suppose a computer program forrecognizing dogs in photographs identifies eight dogs in a picturecontaining 12 dogs and some cats. Of the eight dogs identified, fiveactually are dogs (true positives), while the rest are cats (falsepositives). The program's precision is ⅝ while its recall is 5/12. Whena search engine returns 30 pages only 20 of which were relevant whilefailing to return 40 additional relevant pages, its precision is 20/30=⅔while its recall is 20/60=⅓. Therefore, in this case, precision is ‘howuseful the search results are’, and recall is ‘how complete the resultsare.’” The F1 score (also F-score or F-measure) is a measure of a test'saccuracy. It considers both the precision and the recall of the test tocompute the score: F1=2×((precision×recall)/(precision+recall)) and isthe harmonic mean of precision and recall. The F1 score reaches its bestvalue at 1 (perfect precision and recall) and worst at 0.

Autonomous Agents or Chatbots

A conversation between Human A and Human B is a form of discourse. Forexample, applications exist such as FaceBook® Messenger, WhatsApp®,Slack,® SMS, etc., a conversation between A and B may typically be viamessages in addition to more traditional email and voice conversations.A chatbot (which may also be called intelligent bots or virtualassistant, etc.) is an “intelligent” machine that, for example, replaceshuman B and to various degrees mimics the conversation between twohumans. An example ultimate goal is that human A cannot tell whether Bis a human or a machine (the Turning test, developed by Alan Turing in1950). Discourse analysis, artificial intelligence, including machinelearning, and natural language processing, have made great stridestoward the long-term goal of passing the Turing test. Of course, withcomputers being more and more capable of searching and processing vastrepositories of data and performing complex analysis on the data toinclude predictive analysis, the long-term goal is the chatbot beinghuman-like and a computer combined.

For example, users can interact with the Intelligent Bots Platformthrough a conversational interaction. This interaction, also called theconversational user interface (UI), is a dialog between the end user andthe chatbot, just as between two human beings. It could be as simple asthe end user saying “Hello” to the chatbot and the chatbot respondingwith a “Hi” and asking the user how it can help, or it could be atransactional interaction in a banking chatbot, such as transferringmoney from one account to the other, or an informational interaction ina HR chatbot, such as checking for vacation balance, or asking an FAQ ina retail chatbot, such as how to handle returns. Natural languageprocessing (NLP) and machine learning (ML) algorithms combined withother approaches can be used to classify end user intent. An intent at ahigh level is what the end user would like to accomplish (e.g., getaccount balance, make a purchase). An intent is essentially, a mappingof customer input to a unit of work that the backend should perform.Therefore, based on the phrases uttered by the user in the chatbot,these are mapped that to a specific and discrete use case or unit ofwork, for e.g. check balance, transfer money and track spending are all“use cases” that the chatbot should support and be able to work outwhich unit of work should be triggered from the free text entry that theend user types in a natural language.

The underlying rational for having an AI chatbot respond like a human isthat the human brain can formulate and understand the request and thengive a good response to the human request much better than a machine.Thus, there should be significant improvement in the request/response ofa chatbot, if human B is mimicked. So an initial part of the problem ishow does the human brain formulate and understand the request? To mimic,a model is used. RST and DT allow a formal and repeatable way of doingthis.

At a high level, there are typically two types of requests: (1) Arequest to perform some action; and (2) a request for information, e.g.,a question. The first type has a response in which a unit of work iscreated. The second type has a response that is, e.g., a good answer, tothe question. The answer could take the form of, for example, in someaspects, the AI constructing an answer from its extensive knowledgebase(s) or from matching the best existing answer from searching theinternet or intranet or other publically/privately available datasources.

Communicative Discourse Trees and the Rhetoric Classifier

Aspects of the present disclosure build communicative discourse treesand use communicative discourse trees to analyze whether the rhetoricalstructure of a request or question agrees with an answer. Morespecifically, aspects described herein create representations of arequest-response pair, learns the representations, and relates the pairsinto classes of valid or invalid pairs. In this manner, an autonomousagent can receive a question from a user, process the question, forexample, by searching for multiple answers, determine the best answerfrom the answers, and provide the answer to the user.

More specifically, to represent linguistic features of text, aspectsdescribed herein use rhetoric relations and speech acts (orcommunicative actions). Rhetoric relations are relationships between theparts of the sentences, typically obtained from a discourse tree. Speechacts are obtained as verbs from a verb resource such as VerbNet. Byusing both rhetoric relations and communicative actions, aspectsdescribed herein can correctly recognize valid request-response pairs.To do so, aspects correlate the syntactic structure of a question withthat of an answer. By using the structure, a better answer can bedetermined.

For example, when an autonomous agent receives an indication from aperson that the person desires to sell an item with certain features,the autonomous agent should provide a search result that not onlycontains the features but also indicates an intent to buy. In thismanner, the autonomous agent has determined the user's intent.Similarly, when an autonomous agent receives a request from a person toshare knowledge about a particular item, the search result shouldcontain an intent to receive a recommendation. When a person asks anautonomous agent for an opinion about a subject, the autonomous agentshares an opinion about the subject, rather than soliciting anotheropinion.

Analyzing Request and Response Pairs

FIG. 7 depicts an exemplary DT for an example request about property taxin accordance with an aspect. The node labels are the relations and thearrowed line points to the satellite. The nucleus is a solid line. FIG.7 depicts the following text.

Request: “My husbands' grandmother gave him his grandfather's truck. Shesigned the title over but due to my husband having unpaid fines on hislicense, he was not able to get the truck put in his name. I wanted toput in my name and paid the property tax and got insurance for thetruck. By the time it came to sending off the title and getting the tag,I didn't have the money to do so. Now, due to circumstances, I am notgoing to be able to afford the truck. I went to the insurance place andwas refused a refund. I am just wondering that since I am not going tohave a tag on this truck, is it possible to get the property taxrefunded?”

Response: “The property tax is assessed on property that you own. Justbecause you chose to not register it does not mean that you don't ownit, so the tax is not refundable. Even if you have not titled thevehicle yet, you still own it within the boundaries of the tax district,so the tax is payable. Note that all states give you a limited amount oftime to transfer title and pay the use tax. If you apply late, therewill be penalties on top of the normal taxes and fees. You don't need toregister it at the same time, but you absolutely need to title it withinthe period of time stipulated in state law.”

As can be seen in FIG. 7 , analyzing the above text results in thefollowing. “My husbands' grandmother gave him his grandfather's truck”is elaborated by “She signed the title over but due to my husband”elaborated by “having unpaid fines on his license, he was not able toget the truck put in his name.” which is elaborated by “I wanted to putin my name,” “and paid the property tax”, and “and got insurance for thetruck.”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck.” iselaborated by;

“I didn't have the money” elaborated by “to do so” contrasted with[0104] “By the time” elaborated by “it came to sending off the title”

“and getting the tag”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck. By thetime it came to sending off the title and getting the tag, I didn't havethe money to do so” is contrasted with

“Now, due to circumstances,” elaborated with “I am not going to be ableto afford the truck.” which is elaborated with

“I went to the insurance place”

“and was refused a refund”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck. By thetime it came to sending off the title and getting the tag, I didn't havethe money to do so. Now, due to circumstances, I am not going to be ableto afford the truck. I went to the insurance place and was refused arefund.” is elaborated with

“I am just wondering that since I am not going to have a tag on thistruck, is it possible to get the property tax refunded?”

“I am just wondering” has attribution to

“that” is the same unit as “is it possible to get the property taxrefunded?” which has condition “since I am not going to have a tag onthis truck”

As can be seen, the main subject of the topic is “Property tax on acar”. The question includes the contradiction: on one hand, allproperties are taxable, and on the other hand, the ownership is somewhatincomplete. A good response has to address both topic of the questionand clarify the inconsistency. To do that, the responder is making evenstronger claim concerning the necessity to pay tax on whatever is ownedirrespectively of the registration status. This example is a member ofpositive training set from our Yahoo! Answers evaluation domain. Themain subject of the topic is “Property tax on a car”. The questionincludes the contradiction: on one hand, all properties are taxable, andon the other hand, the ownership is somewhat incomplete. A goodanswer/response has to address both topic of the question and clarifythe inconsistency. The reader can observe that since the questionincludes rhetoric relation of contrast, the answer has to match it witha similar relation to be convincing. Otherwise, this answer would lookincomplete even to those who are not domain experts.

FIG. 8 depicts an exemplary response for the question represented inFIG. 7 , according to certain aspects of the present invention. Thecentral nucleus is “the property tax is assessed on property” elaboratedby “that you own”. “The property tax is assessed on property that youown” is also a nucleus elaborated by “Just because you chose to notregister it does not mean that you don't own it, so the tax is notrefundable. Even if you have not titled the vehicle yet, you still ownit within the boundaries of the tax district, so the tax is payable.Note that all states give you a limited amount of time to transfer titleand pay the use tax.”

The nucleus “The property tax is assessed on property that you own. Justbecause you chose to not register it does not mean that you don't ownit, so the tax is not refundable. Even if you have not titled thevehicle yet, you still own it within the boundaries of the tax district,so the tax is payable. Note that all states give you a limited amount oftime to transfer title and pay the use tax.” is elaborated by “therewill be penalties on top of the normal taxes and fees” with condition“If you apply late,” which in turn is elaborated by the contrast of “butyou absolutely need to title it within the period of time stipulated instate law.” and “You don't need to register it at the same time.”.

Comparing the DT of FIG. 7 and DT of FIG. 8 , enables a determination ofhow well matched the response (FIG. 8 ) is to the request (FIG. 7 ). Insome aspects of the present invention, the above framework is used, atleast in part, to determine the DTs for the request/response and therhetoric agreement between the DTs.

In another example, the question “What does The Investigative Committeeof the Russian Federation do” has at least two answers, for example, anofficial answer or an actual answer.

FIG. 9 illustrates a discourse tree for an official answer in accordancewith an aspect. As depicted in FIG. 9 , an official answer, or missionstatement states that “The Investigative Committee of the RussianFederation is the main federal investigating authority which operates asRussia's Anti-corruption agency and has statutory responsibility forinspecting the police forces, combating police corruption and policemisconduct, is responsible for conducting investigations into localauthorities and federal governmental bodies.”

FIG. 10 illustrates a discourse tree for a raw answer in accordance withan aspect. As depicted in FIG. 10 , another, perhaps more honest, answerstates that “Investigative Committee of the Russian Federation issupposed to fight corruption. However, top-rank officers of theInvestigative Committee of the Russian Federation are charged withcreation of a criminal community. Not only that, but their involvementin large bribes, money laundering, obstruction of justice, abuse ofpower, extortion, and racketeering has been reported. Due to theactivities of these officers, dozens of high-profile cases including theones against criminal lords had been ultimately ruined.”

The choice of answers depends on context. Rhetoric structure allowsdifferentiating between “official”, “politically correct”,template-based answers and “actual”, “raw”, “reports from the field”, or“controversial” answers. (See FIG. 9 and FIG. 10 ). Sometimes, thequestion itself can give a hint about which category of answers isexpected. If a question is formulated as a factoid or definitional one,without a second meaning, then the first category of answers issuitable. Otherwise, if a question has the meaning “tell me what itreally is”, then the second category is appropriate. In general, afterextracting a rhetoric structure from a question, selecting a suitableanswer that would have a similar, matching, or complementary rhetoricstructure is easier.

The official answer is based on elaboration and joints, which areneutral in terms of controversy a text might contain (See FIG. 9 ). Atthe same time, the row answer includes the contrast relation. Thisrelation is extracted between the phrase for what an agent is expectedto do and what this agent was discovered to have done.

Classification of Request-Response Pairs

Rhetoric classification application 102 can determine whether a givenanswer or response, such as an answer obtained from answer database 105or a public database, is responsive to a given question, or request.More specifically, rhetoric classification application 102 analyzeswhether a request and response pair is correct or incorrect bydetermining one or both of (i) relevance or (ii) rhetoric agreementbetween the request and the response. Rhetoric agreement can be analyzedwithout taking into account relevance, which can be treatedorthogonally.

Rhetoric classification application 102 can determine similarity betweenquestion-answer pairs using different methods. For example, rhetoricclassification application 102 can determine level of similarity betweenan individual question and an individual answer. Alternatively, rhetoricclassification application 102 can determine a measure of similaritybetween a first pair including a question and an answer, and a secondpair including a question and answer.

For example, rhetoric classification application 102 uses rhetoricagreement classifier 120 trained to predict matching or non-matchinganswers. Rhetoric classification application 102 can process two pairsat a time, for example <q1, a1> and <q2, a2>. Rhetoric classificationapplication 102 compares q1 with q2 and a1 with a1, producing a combinedsimilarity score. Such a comparison allows a determination of whether anunknown question/answer pair contains a correct answer or not byassessing a distance from another question/answer pair with a knownlabel. In particular, an unlabeled pair <q2, a2> can be processed sothat rather than “guessing” correctness based on words or structuresshared by q2 and a2, both q2 and a2 can be compared with theircorresponding components q1 and a2 of the labeled pair <q2, a2> on thegrounds of such words or structures. Because this approach targets adomain-independent classification of an answer, only the structuralcohesiveness between a question and answer can be leveraged, not‘meanings’ of answers.

In an aspect, rhetoric classification application 102 uses training data125 to train rhetoric agreement classifier 120. In this manner, rhetoricagreement classifier 120 is trained to determine a similarity betweenpairs of questions and answers. This is a classification problem.Training data 125 can include a positive training set and a negativetraining set. Training data 125 includes matching request-response pairsin a positive dataset and arbitrary or lower relevance orappropriateness request-response pairs in a negative dataset. For thepositive dataset, various domains with distinct acceptance criteria areselected that indicate whether an answer or response is suitable for thequestion.

Each training data set includes a set of training pairs. Each trainingset includes a question communicative discourse tree that represents aquestion and an answer communicative discourse tree that represents ananswer and an expected level of complementarity between the question andanswer. By using an iterative process, rhetoric classificationapplication 102 provides a training pair to rhetoric agreementclassifier 120 and receives, from the model, a level of complementarity.Rhetoric classification application 102 calculates a loss function bydetermining a difference between the determined level of complementarityand an expected level of complementarity for the particular trainingpair. Based on the loss function, rhetoric classification application102 adjusts internal parameters of the classification model to minimizethe loss function.

Acceptance criteria can vary by application. For example, acceptancecriteria may be low for community question answering, automated questionanswering, automated and manual customer support systems, social networkcommunications and writing by individuals such as consumers about theirexperience with products, such as reviews and complaints. RR acceptancecriteria may be high in scientific texts, professional journalism,health and legal documents in the form of FAQ, professional socialnetworks such as “stackoverflow.”

Communicative Discourse Trees (CDTs)

Rhetoric classification application 102 can create, analyze, and comparecommunicative discourse trees. Communicative discourse trees aredesigned to combine rhetoric information with speech act structures.CDTs include with arcs labeled with expressions for communicativeactions. By combining communicative actions, CDTs enable the modeling ofRST relations and communicative actions. A CDT is a reduction of a parsethicket. See Galitsky, B, Ilvovsky, D. and Kuznetsov S O. Rhetoric Mapof an Answer to Compound Queries Knowledge Trail Inc. ACL 2015, 681-686.(“Galitsky 2015”). A parse thicket is a combination of parse trees forsentences with discourse-level relationships between words and parts ofthe sentence in one graph. By incorporating labels that identify speechactions, learning of communicative discourse trees can occur over aricher features set than just rhetoric relations and syntax ofelementary discourse units (EDUs).

In an example, a dispute between three parties concerning the causes ofa downing of a commercial airliner, Malaysia Airlines Flight 17 isanalyzed. An RST representation of the arguments being communicated isbuilt. In the example, three conflicting agents, Dutch investigators,The Investigative Committee of the Russian Federation, and theself-proclaimed Donetsk People's Republic exchange their opinions on thematter. The example illustrates a controversial conflict where eachparty does all it can to blame its opponent. To sound more convincing,each party does not just produce its claim but formulates a response ina way to rebuff the claims of an opponent. To achieve this goal, eachparty attempts to match the style and discourse of the opponents'claims.

FIG. 11 illustrates a communicative discourse tree for a claim of afirst agent in accordance with an aspect. FIG. 11 depicts communicativediscourse tree 100, which represents the following text: “Dutch accidentinvestigators say that evidence points to pro-Russian rebels as beingresponsible for shooting down plane. The report indicates where themissile was fired from and identifies who was in control of theterritory and pins the downing of MI-117 on the pro-Russian rebels.”

As can be seen from FIG. 11 , non-terminal nodes of CDTs are rhetoricrelations, and terminal nodes are elementary discourse units (phrases,sentence fragments) which are the subjects of these relations. Certainarcs of CDTs are labeled with the expressions for communicative actions,including the actor agent and the subject of these actions (what isbeing communicated). For example, the nucleus node for elaborationrelation (on the left) are labeled with say (Dutch, evidence), and thesatellite with responsible(rebels, shooting down). These labels are notintended to express that the subjects of EDUs are evidence and shootingdown but instead for matching this CDT with others for the purpose offinding similarity between them. In this case just linking thesecommunicative actions by a rhetoric relation and not providinginformation of communicative discourse would be too limited way torepresent a structure of what and how is being communicated. Arequirement for an RR pair to have the same or coordinated rhetoricrelation is too weak, so an agreement of CDT labels for arcs on top ofmatching nodes is required.

The straight edges of this graph are syntactic relations, and curvy arcsare discourse relations, such as anaphora, same entity, sub-entity,rhetoric relation and communicative actions. This graph includes muchricher information than just a combination of parse trees for individualsentences. In addition to CDTs, parse thickets can be generalized at thelevel of words, relations, phrases and sentences. The speech actions arelogic predicates expressing the agents involved in the respective speechacts and their subjects. The arguments of logical predicates are formedin accordance to respective semantic roles, as proposed by a frameworksuch as VerbNet. See Karin Kipper, Anna Korhonen, Neville Ryant, MarthaPalmer, A Large-scale Classification of English Verbs, LanguageResources and Evaluation Journal, 42(1), pp. 21-40, Springer Netherland,2008. and/or Karin Kipper Schuler, Anna Korhonen, Susan W. Brown,VerbNet overview, extensions, mappings and apps, Tutorial, NAACL-HLT2009, Boulder, Colo.

FIG. 12 illustrates a communicative discourse tree for a claim of asecond agent in accordance with an aspect. FIG. 12 depicts communicativediscourse tree 1200, which represents the following text: “TheInvestigative Committee of the Russian Federation believes that theplane was hit by a missile, which was not produced in Russia. Thecommittee cites an investigation that established the type of themissile.”

FIG. 13 illustrates a communicative discourse tree for a claim of athird agent in accordance with an aspect. FIG. 13 depicts communicativediscourse tree 1300, which represents the following text: “Rebels, theself-proclaimed Donetsk People's Republic, deny that they controlled theterritory from which the missile was allegedly fired. It became possibleonly after three months after the tragedy to say if rebels controlledone or another town.”

As can be seen from communicative discourse trees 1100-1300, a responseis not arbitrary. A response talks about the same entities as theoriginal text. For example, communicative discourse trees 1200 and 1300are related to communicative discourse tree 1100. A response backs up adisagreement with estimates and sentiments about these entities, andabout actions of these entities.

More specifically, replies of involved agent need to reflect thecommunicative discourse of the first, seed message. As a simpleobservation, because the first agent uses Attribution to communicate hisclaims, the other agents have to follow the suite and either providetheir own attributions or attack the validity of attribution of theproponent, or both. To capture a broad variety of features for howcommunicative structure of the seed message needs to be retained inconsecutive messages, pairs of respective CDTs can be learned.

To verify the agreement of a request-response, discourse relations orspeech acts (communicative actions) alone are often insufficient. As canbe seen from the example depicted in FIGS. 11-13 , the discoursestructure of interactions between agents and the kind of interactionsare useful. However, the domain of interaction (e.g., military conflictsor politics) or the subjects of these interactions, i.e., the entities,do not need to be analyzed.

Representing Rhetoric Relations and Communicative Actions

In order to compute similarity between abstract structures, twoapproaches are frequently used: (1) representing these structures in anumerical space, and express similarity as a number, which is astatistical learning approach, or (2) using a structural representation,without numerical space, such as trees and graphs, and expressingsimilarity as a maximal common sub-structure. Expressing similarity as amaximal common sub-structure is referred to as generalization.

Learning communicative actions helps express and understand arguments.Computational verb lexicons help support acquisition of entities foractions and provide a rule-based form to express their meanings. Verbsexpress the semantics of an event being described as well as therelational information among participants in that event, and project thesyntactic structures that encode that information. Verbs, and inparticular communicative action verbs, can be highly variable and candisplay a rich range of semantic behaviors. In response, verbclassification helps a learning systems to deal with this complexity byorganizing verbs into groups that share core semantic properties.

VerbNet is one such lexicon, which identifies semantic roles andsyntactic patterns characteristic of the verbs in each class and makesexplicit the connections between the syntactic patterns and theunderlying semantic relations that can be inferred for all members ofthe class. See Karin Kipper, Anna Korhonen, Neville Ryant and MarthaPalmer, Language Resources and Evaluation, Vol. 42, No. 1 (March 2008),at 21. Each syntactic frame, or verb signature, for a class has acorresponding semantic representation that details the semanticrelations between event participants across the course of the event.

For example, the verb amuse is part of a cluster of similar verbs thathave a similar structure of arguments (semantic roles) such as amaze,anger, arouse, disturb, and irritate. The roles of the arguments ofthese communicative actions are as follows: Experiencer (usually, ananimate entity), Stimulus, and Result. Each verb can have classes ofmeanings differentiated by syntactic features for how this verb occursin a sentence, or frames. For example, the frames for amuse are asfollows, using the following key noun phrase (NP), noun (N),communicative action (V), verb phrase (VP), adverb (ADV):

NP V NP. Example: “The teacher amused the children.” Syntax: Stimulus VExperiencer. Clause: amuse(Stimulus, E, Emotion, Experiencer),cause(Stimulus, E), emotional_state(result(E), Emotion, Experiencer).

NP V ADV-Middle. Example: “Small children amuse quickly.” Syntax:Experiencer V ADV. Clause: amuse(Experiencer, Prop):-,property(Experiencer, Prop), adv(Prop).

NP V NP-PRO-ARB. Example “The teacher amused.” Syntax Stimulus V.amuse(Stimulus, E, Emotion, Experiencer):. cause(Stimulus, E),emotional_state(result(E), Emotion, Experiencer).

NP.cause V NP. Example “The teacher's dolls amused the children.” syntaxStimulus <+genitive>('s) V Experiencer. amuse(Stimulus, E, Emotion,Experiencer):. cause(Stimulus, E), emotional_state(during(E), Emotion,Experiencer).

NP V NP ADJ. Example “This performance bored me totally.” syntaxStimulus V Experiencer Result. amuse(Stimulus, E, Emotion, Experiencer).cause(Stimulus, E),emotional_state(result(E), Emotion, Experiencer),Pred(result(E), Experiencer).

Communicative actions can be characterized into clusters, for example:

Verbs with Predicative Complements (Appoint, characterize, dub, declare,conjecture, masquerade, orphan, captain, consider, classify), Verbs ofPerception (See, sight, peer).

Verbs of Psychological State (Amuse, admire, marvel, appeal), Verbs ofDesire (Want, long).

Judgment Verbs (Judgment), Verbs of Assessment (Assess, estimate), Verbsof Searching (Hunt, search, stalk, investigate, rummage, ferret), Verbsof Social Interaction (Correspond, marry, meet, battle), Verbs ofCommunication (Transfer(message), inquire, interrogate, tell,manner(speaking), talk, chat, say, complain, advise, confess, lecture,overstate, promise). Avoid Verbs (Avoid), Measure Verbs, (Register,cost, fit, price, bill), Aspectual Verbs (Begin, complete, continue,stop, establish, sustain.

Aspects described herein provide advantages over statistical learningmodels. In contrast to statistical solutions, aspects use aclassification system can provide a verb or a verb-like structure whichis determined to cause the target feature (such as rhetoric agreement).For example, statistical machine learning models express similarity as anumber, which can make interpretation difficult.

Representing Request-Response Pairs

Representing request-response pairs facilitates classification basedoperations based on a pair. In an example, request-response pairs can berepresented as parse thickets. A parse thicket is a representation ofparse trees for two or more sentences with discourse-level relationshipsbetween words and parts of the sentence in one graph. See Galitsky 2015.Topical similarity between question and answer can expressed as commonsub-graphs of parse thickets. The higher the number of common graphnodes, the higher the similarity.

FIG. 14 illustrates parse thickets in accordance with an aspect. FIG. 14depicts parse thicket 1400 including a parse tree for a request 1401,and a parse tree for a corresponding response 1402.

Parse tree 1401 represents the question “I just had a baby and it looksmore like the husband I had my baby with. However it does not look likeme at all and I am scared that he was cheating on me with another ladyand I had her kid. This child is the best thing that has ever happenedto me and I cannot imagine giving my baby to the real mom.”

Response 1402 represents the response “Marital therapists advise ondealing with a child being born from an affair as follows. One option isfor the husband to avoid contact but just have the basic legal andfinancial commitments. Another option is to have the wife fully involvedand have the baby fully integrated into the family just like a childfrom a previous marriage.”

FIG. 14 represents a greedy approach to representing linguisticinformation about a paragraph of text. The straight edges of this graphare syntactic relations, and curvy arcs are discourse relations, such asanaphora, same entity, sub-entity, rhetoric relation and communicativeactions. The solid arcs are for same entity/sub-entity/anaphorarelations, and the dotted arcs are for rhetoric relations andcommunicative actions. Oval labels in straight edges denote thesyntactic relations. Lemmas are written in the boxes for the nodes, andlemma forms are written on the right side of the nodes.

Parse thicket 1400 includes much richer information than just acombination of parse trees for individual sentences. Navigation throughthis graph along the edges for syntactic relations as well as arcs fordiscourse relations allows to transform a given parse thicket intosemantically equivalent forms for matching with other parse thickets,performing a text similarity assessment task. To form a complete formalrepresentation of a paragraph, as many links as possible are expressed.Each of the discourse arcs produces a pair of thicket phrases that canbe a potential match.

Topical similarity between the seed (request) and response is expressedas common sub-graphs of parse thickets. They are visualized as connectedclouds. The higher the number of common graph nodes, the higher thesimilarity. For rhetoric agreement, common sub-graph does not have to belarge as it is in the given text. However, rhetoric relations andcommunicative actions of the seed and response are correlated and acorrespondence is required.

Generalization for Communicative Actions

A similarity between two communicative actions A₁ and A₂ is defined as aan abstract verb which possesses the features which are common betweenA₁ and A₂. Defining a similarity of two verbs as an abstract verb-likestructure supports inductive learning tasks, such as a rhetoricagreement assessment. In an example, a similarity between the followingtwo common verbs, agree and disagree, can be generalized as follows:agree{circumflex over ( )}disagree=verb(Interlocutor, Proposed_action,Speaker), where Interlocution is the person who proposed theProposed_action to the Speaker and to whom the Speaker communicatestheir response. Proposed_action is an action that the Speaker wouldperform if they were to accept or refuse the request or offer, and TheSpeaker is the person to whom a particular action has been proposed andwho responds to the request or offer made.

In a further example, a similarity between verbs agree and explain isrepresented as follows: agree{circumflex over( )}explain=verb(Interlocutor, *, Speaker). The subjects ofcommunicative actions are generalized in the context of communicativeactions and are not be generalized with other “physical” actions. Hence,aspects generalize individual occurrences of communicative actionstogether with corresponding subjects.

Additionally, sequences of communicative actions representing dialogscan be compared against other such sequences of similar dialogs. In thismanner, the meaning of an individual communicative action as well as thedynamic discourse structure of a dialogue is (in contrast to its staticstructure reflected via rhetoric relations) is represented. Ageneralization is a compound structural representation that happens ateach level. Lemma of a communicative action is generalized with lemma,and its semantic role are generalized with respective semantic role.

Communicative actions are used by text authors to indicate a structureof a dialogue or a conflict. See Searle, J. R. 1969, Speech acts: anessay in the philosophy of language. London: Cambridge University Press.Subjects are generalized in the context of these actions and are notgeneralized with other “physical” actions. Hence, the individualoccurrences of communicative actions together are generalized with theirsubjects, as well as their pairs, as discourse “steps.”

Generalization of communicative actions can also be thought of from thestandpoint of matching the verb frames, such as VerbNet. Thecommunicative links reflect the discourse structure associated withparticipation (or mentioning) of more than a single agent in the text.The links form a sequence connecting the words for communicative actions(either verbs or multi-words implicitly indicating a communicativeintent of a person).

Communicative actions include an actor, one or more agents being actedupon, and the phrase describing the features of this action. Acommunicative action can be described as a function of the form: verb(agent, subject, cause), where verb characterizes some type ofinteraction between involved agents (e.g., explain, confirm, remind,disagree, deny, etc.), subject refers to the information transmitted orobject described, and cause refers to the motivation or explanation forthe subject.

A scenario (labeled directed graph) is a sub-graph of a parse thicketG=(V, A), where V={action₁, action₂ . . . action_(n)} is a finite set ofvertices corresponding to communicative actions, and A is a finite setof labeled arcs (ordered pairs of vertices), classified as follows:

Each arc action_(i), action_(i)∈A_(sequence) corresponds to a temporalprecedence of two actions v_(i), ag_(i), s_(i), c_(i) and v_(j), ag_(j),s_(j), c_(j) that refer to the same subject, e.g., s_(j)=s_(i) ordifferent subjects. Each arc action_(i), action_(j)∈A_(cause)corresponds to an attack relationship between action_(i) and action_(j)indicating that the cause of action_(i) in conflict with the subject orcause of action_(j).

Subgraphs of parse thickets associated with scenarios of interactionbetween agents have some distinguishing features. For example, (1) allvertices are ordered in time, so that there is one incoming arc and oneoutgoing arc for all vertices (except the initial and terminalvertices), (2) for A_(sequence) arcs, at most one incoming and only oneoutgoing arc are admissible, and (3) for A_(cause) arcs, there can bemany outgoing arcs from a given vertex, as well as many incoming arcs.The vertices involved may be associated with different agents or withthe same agent (i.e., when he contradicts himself). To computesimilarities between parse thickets and their communicative action,induced subgraphs, the sub-graphs of the same configuration with similarlabels of arcs and strict correspondence of vertices are analyzed.

The following similarities exist by analyzing the arcs of thecommunicative actions of a parse thicket: (1) one communicative actionfrom with its subject from T1 against another communicative action withits subject from T2 (communicative action arc is not used), and (2) apair of communicative actions with their subjects from T1 compared toanother pair of communicative actions from T2 (communicative action arcsare used).

Generalizing two different communicative actions is based on theirattributes. See (Galitsky et al 2013). As can be seen in the examplediscussed with respect to FIG. 14 , one communicative action from T1,cheating(husband, wife, another lady) can be compared with a second fromT2, avoid(husband, contact(husband, another lady)). A generalizationresults in communicative_action(husband, *) which introduces aconstraint on A in the form that if a given agent (=husband) ismentioned as a subject of CA in Q, he(she) should also be a subject of(possibly, another) CA in A. Two communicative actions can always begeneralized, which is not the case for their subjects: if theirgeneralization result is empty, the generalization result ofcommunicative actions with these subjects is also empty.

Generalization of RST Relations

Some relations between discourse trees can be generalized, such as arcsthat represent the same type of relation (presentation relation, such asantithesis, subject matter relation, such as condition, and multinuclearrelation, such as list) can be generalized. A nucleus or a situationpresented by a nucleus is indicated by “N.” Satellite or situationspresented by a satellite, are indicated by “S.” “W” indicates a writer.“R” indicates a reader (hearer). Situations are propositions, completedactions or actions in progress, and communicative actions and states(including beliefs, desires, approve, explain, reconcile and others).Generalization of two RST relations with the above parameters isexpressed as:rst1(N1,S1,W1,R1){circumflex over ( )}rst2(N2,S2,W2,R2)=(rst1{circumflexover (r)}st2)(N1{circumflex over ( )}N2,S1{circumflex over( )}S2,W1{circumflex over ( )}W2,R1{circumflex over ( )}R2).

The texts in N1, S1, W1, R1 are subject to generalization as phrases.For example, rst1{circumflex over ( )}rst2 can be generalized asfollows: (1) if relation_type(rst1)!=relation_type(rst2) then ageneralization is empty. (2) Otherwise, the signatures of rhetoricrelations are generalized as sentences: sentence(N1, S1, W1,R1){circumflex over ( )}sentence(N2, S2, W2, R2). See Iruskieta, Mikel,Iria da Cunha and Maite Taboada. A qualitative comparison method forrhetorical structures: identifying different discourse structures inmultilingual corpora. Lang Resources & Evaluation. June 2015, Volume 49,Issue 2.

For example, the meaning of rst−background{circumflex over( )}rst−enablement=(S increases the ability of R to comprehend anelement in N){circumflex over ( )}(R comprehending S increases theability of R to perform the action in N)=increase−VB the−DT ability−NNof−IN R−NN to−IN.

Because the relations rst−background{circumflex over ( )}rst−enablementdiffer, the RST relation part is empty. The expressions that are theverbal definitions of respective RST relations are then generalized. Forexample, for each word or a placeholder for a word such as an agent,this word (with its POS) is retained if the word the same in each inputphrase or remove the word if the word is different between thesephrases. The resultant expression can be interpreted as a common meaningbetween the definitions of two different RST relations, obtainedformally.

Two arcs between the question and the answer depicted in FIG. 14 showthe generalization instance based on the RST relation “RST-contrast”.For example, “I just had a baby” is a RST-contrast with “it does notlook like me,” and related to “husband to avoid contact” which is aRST-contrast with “have the basic legal and financial commitments.” Ascan be seen, the answer need not have to be similar to the verb phraseof the question but the rhetoric structure of the question and answerare similar. Not all phrases in the answer must match phrases inquestion. For example, the phrases that do not match have certainrhetoric relations with the phrases in the answer which are relevant tophrases in question.

Building a Communicative Discourse Tree

FIG. 15 illustrates an exemplary process for building a communicativediscourse tree in accordance with an aspect. Rhetoric classificationapplication 102 can implement process 1500. As discussed, communicativediscourse trees enable improved search engine results.

At block 1501, process 1500 involves accessing a sentence includingfragments. At least one fragment includes a verb and words and each wordincludes a role of the words within the fragment, and each fragment isan elementary discourse unit. For example, rhetoric classificationapplication 102 accesses a sentence such as “Rebels, the self-proclaimedDonetsk People's Republic, deny that they controlled the territory fromwhich the missile was allegedly fired” as described with respect to FIG.13 .

Continuing the example, rhetoric classification application 102determines that the sentence includes several fragments. For example, afirst fragment is “rebels . . . deny.” A second fragment is “that theycontrolled the territory.” A third fragment is “from which the missilewas allegedly fired.” Each fragment includes a verb, for example, “deny”for the first fragment and “controlled” for the second fragment.Although, a fragment need not include a verb.

At block 1502, process 1500 involves generating a discourse tree thatrepresents rhetorical relationships between the sentence fragments. Thediscourse tree including nodes, each nonterminal node representing arhetorical relationship between two of the sentence fragments and eachterminal node of the nodes of the discourse tree is associated with oneof the sentence fragments.

Continuing the example, rhetoric classification application 102generates a discourse tree as shown in FIG. 13 . For example, the thirdfragment, “from which the missile was allegedly fired” elaborates on“that they controlled the territory.” The second and third fragmentstogether relate to attribution of what happened, i.e., the attack cannothave been the rebels because they do not control the territory.

At block 1503, process 1500 involves accessing multiple verb signatures.For example, rhetoric classification application 102 accesses a list ofverbs, e.g., from VerbNet. Each verb matches or is related to the verbof the fragment. For example, the for the first fragment, the verb is“deny.” Accordingly, rhetoric classification application 102 accesses alist of verb signatures that relate to the verb deny.

As discussed, each verb signature includes the verb of the fragment andone or more of thematic roles. For example, a signature includes one ormore of noun phrase (NP), noun (N), communicative action (V), verbphrase (VP), or adverb (ADV). The thematic roles describing therelationship between the verb and related words. For example “theteacher amused the children” has a different signature from “smallchildren amuse quickly.” For the first fragment, the verb “deny,”rhetoric classification application 102 accesses a list of frames, orverb signatures for verbs that match “deny.” The list is “NP V NP to beNP,” “NP V that S” and “NP V NP.”

Each verb signature includes thematic roles. A thematic role refers tothe role of the verb in the sentence fragment. Rhetoric classificationapplication 102 determines the thematic roles in each verb signature.Example thematic roles include actor, agent, asset, attribute,beneficiary, cause, location destination source, destination, source,location, experiencer, extent, instrument, material and product,material, product, patient, predicate, recipient, stimulus, theme, time,or topic.

At block 1504, process 1500 involves determining, for each verbsignature of the verb signatures, a number of thematic roles of therespective signature that match a role of a word in the fragment. Forthe first fragment, rhetorical classification application 102 determinesthat the verb “deny” has only three roles, “agent”, “verb” and “theme.”

At block 1505, process 1500 involves selecting a particular verbsignature from the verb signatures based on the particular verbsignature having a highest number of matches. For example, referringagain to FIG. 13 , deny in the first fragment “the rebels deny . . .that they control the territory” is matched to verb signature deny “NP VNP”, and “control” is matched to control (rebel, territory). Verbsignatures are nested, resulting in a nested signature of “deny(rebel,control(rebel, territory)).”

Concluding a Question Answering Session

As discussed, aspects of the present disclosure construct acomprehensive answer for presentation an interactive session betweenuser and autonomous agent. In some cases, a short and concise answersuch as account balance or person name suffices. This type of answer canbe referred to as a factoid answer. In other cases, a longer answer thataddresses subsequent user utterances is appropriate. This answer, aconclusive answer, is a comprehensive answer that gives a user a chanceto get a deep understanding of an entity or topic. An example of aprocess for determining whether a factoid or conclusive answer isappropriate is discussed further with respect to FIG. 16 .

FIG. 16 illustrates an exemplary process 1600 for determining anappropriate type of answer to be provided by an autonomous agent, inaccordance with an aspect. Process 1600 can be performed by conclusiveanswer application 102. Process 1600 can be executed after one or moreutterances have been received by conclusive answer application 102.

At block 1601, process 1600 involves accessing one or more utterances.After an initial user utterance, for example, a question, a number ofclarification steps usually follow. For example, user device 170transmits one or more utterances to computing device 101. Computingdevice 101 receives the utterance and proceeds to block 1602.

At block 1602, process 1600 involves determining whether a number ofutterances is greater than a threshold. Conclusive answer application102 can decide which kind of answer is most suitable for a givensession. Using a threshold number of utterances is one way to determinewhether a conclusive answer is appropriate. In an example, the thresholdis four utterances. In this example, if four or more utterances arepresent, then a conclusive answer is generated. Based on a thresholdnumber of occurrences being met, then conclusive answer application 102can determine that a conclusive answer should be provided.

Other methods can be used. For example, conclusive answer application102 can determine that a particular entity is present in multiple userutterances, indicating that the user is focused on a particular issue.In an aspect, conclusive answer application 102 can detect that a useraccepted information related to an entity. For example, if conclusiveanswer application 102 responds to a user question about an entity andthe accepts the response, then the conclusive answer application 102 candetermine that a conclusive answer is appropriate. In another aspect,conclusive answer application 102 can detect a mental state from anutterance and determine that a conclusive answer is appropriate based onthe mental state being detected. Mental states can be detected usingtrained machine learning models, using the techniques described herein.

If conclusive answer application 102 determines that a factoid answer isappropriate, then process 1600 proceeds to block 1603. If conclusiveanswer application 102 determines that a conclusive answer isappropriate, then process 1600 proceeds to block 1604.

At block 1603, process 1600 involves generating a factoid answer thatcorresponds to an initial user utterance. A factoid answer can beconstructed based on a structure of how entity and its attributes areintroduced. Conclusive answer application 102 can determine a value ofthe parameter or attribute that corresponds to the entity mentioned bythe user. For a simple user utterance that requests information aboutone entity (e.g., the cost of a non-sufficient funds fee), a factoidanswer is appropriate. Continuing the example, conclusive answerapplication 102 determines that the cost (the attribute) of anon-sufficient funds fee is $29 (the value) in this particular case.

FIG. 17 illustrates an example of a conversation flow with a factoidanswer, in accordance with an aspect. FIG. 17 depicts interaction 1700,which includes question 1701 and agent answer 1702. As can be seen, theuser's question is a relatively factual one and can be addressed by asimple factual answer.

Returning to FIG. 16 , at block 1604, process 1600 involves generating aconclusive answer that addresses multiple user utterances. The goal ofthe conclusive answer is that sufficient information is provided that auser is satisfied with a session with an autonomous agent. If furtherquestions based on this answer arise, the user can start a new sessionkeeping in mind a specific focus. The answer should be as easy toperceive and as intuitive as possible.

Therefore the addition of images, videos and audio files can bebeneficial. The answer compilation method should be domain independentand well-presented. In some cases, a table of contents and referencesare generated. Multiple sources can be used to assure an unbiased,objective description. In the case that the conclusive answer isopinionated, however, multiple opinions from a broad spectrum ofperspectives must be compiled in a coherent manner.

A structure of a conclusive answer can be based on questions,disagreements, and/or misunderstandings about an entity occurring in anutterance. For example, if a user is upset about a non-sufficient fundscharge, then a conclusive answer may be appropriate because the userdesires a more comprehensive answer that explains why the fee wascharged. FIG. 18 illustrates one such case.

FIG. 18 illustrates an example of a conversation flow with a conclusiveanswer, in accordance with an aspect. FIG. 18 depicts interaction 1800,which includes user questions 1801, 1803, and 1805 and agent responses1802, 1804, and 1806. As depicted by FIG. 18 , the user has some issueswith non-sufficient funds fees that go well beyond a simple factualquestion (as illustrated in the example provided by FIG. 17 ).

As can be seen, because the user is frustrated about the non-sufficientfunds fee and is trying to understand why it happened and how to avoidit, a generic answer about an entity would probably frustrate the userbecause the user appears to know general information aboutnon-sufficient funds fees. Therefore the conclusive answer should focuson a specific user issue/misunderstanding exposed in the previousutterances of a dialogue. Here, conclusive answer application provides aconclusive answer, agent response 1806. As can be seen, agent response1806 addresses the user's multiple, but related issues, such as theissue raised by the user that a deposit was made (question 1803) and howto stay positive (question 1805).

At block 1605, process 1600 involves providing the conclusive answer toa user device. For example, conclusive answer application 102 providesthe conclusive answer to user device 170. User device 170 can presentthe answer to a user via display 171.

Preparing Textual Content for Conclusive Answer Generation

In some cases, textual content is prepared for use by conclusive answerapplication 102. Preparation can help speed up access to text and/orremove extraneous or irrelevant content from the text. Conclusive answerapplication 102 can populate and use text corpus 105 with materialgathered from other sources. Examples of sources include customersupport logs, various forms of corresponding with customers or internalissue logs. Determining whether a text is suitable can be done by meansof discourse-level analysis or in a string-based manner. Once chunks oftext are extracted from various sources, the text can be indexed forfaster searching.

FIG. 19 illustrates an exemplary process for converting textual contentfor use in forming a conclusive answer, in accordance with an aspect.Process 1900 can be performed by conclusive answer application 102.

At block 1901, process 1900 involves accessing a document with sections.Various document sources can be used, including the written documentsand web pages explaining entities, their attributes and specifyingbusiness rules. Conclusive answer application 102 can covert the textinto a unified form. In an example, the text can adhere (1) containparagraph-size text (e.g., two to six sentences, 60-150 words) and (2)be self-contained. For example, conclusive answer application 102 canremove any references to previous or subsequent paragraphs.

At block 1902, process 1900 involves determining a style of eachsection. Style can be determined by using communicative discourse trees.For example, a communicative discourse tree can be created for acandidate section of text (e.g., by using process 1500 described in FIG.15 ). The first communicative discourse tree can be provided to amachine learning model that is trained to detect whether thecommunicative discourse tree is similar in style to a reference (ordesired style). Based on a threshold similarity level being reached, thecandidate text is integrated into the text corpus 105.

At block 1903, process 1900 involves identifying and removing unsuitableportions of sections. Text can be extracted from a proper area of awebpage or a proper section of a document, and cleaned. For example, forweb-based content, conclusive answer application 102 determines whetheran article includes desired content (e.g. by matching keywords from thetarget content and the document). If the document does include thedesired content, then conclusive answer application 102 finds acontiguous block of HTML in the webpage starting with the first word inthe article and ending with the last.

Additionally, conclusive answer application can remove everything otherthan the desired text. For example, HTML tags, advertisements, and otherirrelevant content can be removed. When the first word or last word ofdesired content is nested within one or more pairs of HTML tags,conclusive answer application can append the relevant opening and endingto the beginning and ending of the extracted block. If the content isnot nested, then one or more pairs of tags can be left open, disruptingthe article text's formatting. Such cases can be ignored.

At block 1904, process 1900 involves dividing text into paragraphs.Conclusive answer application 102 can divide sentences when a newlinecharacter is detected and/or maintain a minimum and/or maximum paragraphlength (number of sentences).

At block 1905, process 1900 involves indexing the text. Standardindexing techniques can be used.

Building a Structure of a Factoid Answer

The logical structure of an answer reflects the structure of precedingdialogue. For example, if the user has asked questions and/or receivedanswers from the autonomous agent after the initial utterance, thenthese questions or answers is reflected in the structure of theconclusive answer.

Determining the structure of an answer involves determining one or moreentities present in the user utterances. An entity is a noun that refersto a person, place, or thing. An entity can have an attribute. Forexample, if an entity is “non-sufficient funds fee,” then the attributemight be “$29” indicating that the fee is $29. Once a syntactic parsetree is constructed for a user utterance, entities can be identified.For example, noun phrases are first identified. If a noun phraseincludes no more than three words, then it can be considered to form anamed entity.

For a factoid answer, a default structure can be provided. Thisstructure can be mined from the general web sources such as Wikipediaand domain-specific sources such as Investopedia.com. For example, theTOC for the topic Adjusted Gross Margin would use the section structurefrom the respective Investopedia.com page such as the main definitions,treatment in depth, associated topics and others. In this case it ispossible to build TOC in a hierarchical manner. Table 1 belowillustrates an example of a dialog flow in which a user asks about aparticular entity.

TABLE 1 An example of a format for a factoid answer. Section ContentsSection 1 What is Entity E (the topic of the initial user utterance)Section 2 E has its attribute A: the first user clarification requestSection 3 E and its attribute A1 and is it similar to A. Section 4 E andhow it is similar to another entity E1.

When determining the content of the answer, conclusive answerapplication 102 uses the determined structure as a guide. For eachsection, conclusive answer application 102 obtains content thatcorresponds to the entities and attributes determined in the userutterances.

Building a Structure of Conclusive Answer

A conclusive answer includes a section structure that reflects thelogical flow of the user utterances as received so far. Conclusiveanswer application 102 can generate table of contents (TOC) that matchesthe structure.

For example, if a user has a specific concern about an entity, such as‘Why banks can increase APR without advance notice’, then conclusiveanswer application 102 builds a structure to address this concern.Relevant documents can be identified by their respective section titles,which are selected to correspond to the contents of the user utterances.As compared to a factoid answer, the selected documents are identifiedto be relevant not just to the main entity but to the why certainentities are related and/or have certain attributes.

TABLE 2 An example of a format for a conclusive answer. Section ContentsSection 1 What is Entity E (the topic of the initial user utterance)Section 2 Why E has its attribute A: the first user clarificationrequest Section 3 E and its attribute A1 and is it similar to A: thesecond user clarification request Section 4 E and how it is similar toanother entity E1: the user expressed her concern about E2

For example, to form the document structure for the example consideredin FIG. 18 , the following phrases from the user questions can be asqueries to establish the section structure of the conclusive answer: (1)Non-sufficient fund fee (NSF); (2) Why was I charged; (3) Make adeposit; and (4) make a payment for a lower amount. These phrases(extended with synonyms) should match some section structures of certaindocuments about NSF and banking customer support logs: they will form askeleton of the resultant answer.

Conclusive answer application 102 can determine attributes thatcorrespond to a particular entity is to form a structure of a documentis an auto-complete feature for web search. Auto-complete results from asearch engine can be the queries to the text corpus 105 and/or serve assection titles for the table-of-contents. For example, if an entity inthe preceding dialogue is “Edison invented” then the final concludingdocument can include ‘Edison invented the light bulb’, and “Edisoninvented the phonograph.”

Content Compilation for Answers

Once the structure of the conclusive answer has been determined, forexample, by using process 1900, conclusive answer application 102 candetermine relevant content. Content can be determined on a sectionbasis. For example, referring back to Table 1 and Table 2 above, contentcan be determined for the first section, then the second section, and soon. Accordingly, conclusive answer application 102 can execute multipletimes, once for each section.

FIG. 20 illustrates an exemplary process 2000 for building a conclusiveanswer, in accordance with an aspect. Process 2000 can be performedonce, for a single seed sentence, or multiple times, once for each seedsentence, according the appropriate structure of the conclusive answer,for example, as determined by process 1600. A single phrase or multiplephrases of the seed can form one or more paragraphs about the respectivetopics.

At block 2001, process 2000 involves forming a query from an utterance.The utterance includes text fragments. Each fragment can correspond toan elementary discourse unit. To find relevant sentences on the web fora seed sentence, conclusive answer application 102 develops a query.

For example, conclusive answer application 102 can identify any entitieswithin the fragments and then uses the entities as a basis to querycontent sources. Identification of one or more entities can bedetermined generating syntactic parse trees from the utterance. In anexample, significant noun phrases have three or more keywords, e.g., twoor more modifiers for a noun, or an entity, such as a proper noun. Amain entity can be identified by traversing a syntactic parse tree forthe utterance from the top (root) down and identifying the first nounphrase in the syntactic parse tree.

In an example, a user utterance is a seed. The utterance is “Give me abreak, there is no reason why you can't retire in ten years if you hadbeen a rational investor and not a crazy trader.” Conclusive answerapplication 102 identifies the entity in this utterance by creating asyntactic parse tree and identifying a noun phrase that is closest tothe root (or the top) of the parse tree. Here, conclusive answerapplication 102 identifies the main entity as “rational investor.” Theother candidates for the main entity are rejected since they are toobroad (such as retire, a single-word concept), or occur with a negationnot a crazy trader.

Conclusive answer application 102 identifies a main entity as retirementin the form of the verb retire. This main entity is constrained by thenoun phrase that follows rational investor. To form the second query,rational investor and the next noun phrase not a crazy trader arecombined. In some cases, a query with four to five keywords is used.Continuing the example, the following queries are formed for searchengine API:(Q1)+retire+rational+investor(Q2)+rational+investor not+crazy+trader

At block 2002, process 2000 involves providing the query to one or morecontent sources. The content sources can include text corpus 105 orother sources. Examples of content sources include such sources asWikipedia, Bing, Yahoo API or Google, as well as respective newscontent.

Continuing the example, conclusive answer application 102 determines apage for the search “rational investor” by searching Wikipedia. The pageredirects to “Homo economicus,” which contains the following sections(1) the history of the term; (2) Model; (3) Criticisms; (4) Responses;(5) Perspectives; and (6) Homo sociologicus. In some cases, conclusiveanswer application 102 uses each section title. In some cases, if thequeries do not result in enough relevant sentences, the whole sentencecan be used as the query.

At block 2003, process 2000 involves obtaining results. Conclusiveanswer application 102 obtains the results from the search queries. Insome cases, the results are divided into sentences and markers areinserted for any missing information, which can be substituted by textfrom original web pages or documents.

In some cases, only partial results are returned from the search. Ifonly a fragment of sentence is present, then conclusive answerapplication 102 visits the original page, locates the sentence, anddownloads the sentence.

Continuing the example, the following snippet is selected as a candidateto be included in a conclusive answer, since it contains all keywordsfrom Q1. “How to Make Rational Investing Decisions Sound Mind Investing. . . Nov. 1, 2014—How to Make Rational Investing Decisions . . .pleasant and you'll probably have more money to spend in retirement andleave to your heirs.” Conclusive answer application 102 downloads thiswebpage, extracts text from it and find a paragraph which corresponds tothe above snippet. This is continued for all search results whichcontains all keywords from the query. Conclusive answer application 102considers two text fragments from the search results:

(A1a) If you take the time to understand the psychology of rationalinvesting, you'll make your life more pleasant and you'll probably havemore money to spend in retirement and leave to your heirs.

(A1b) One needs many years of relevant data before deciding if a fundmanager is truly skilled in rational investing or just lucky. Hence, bythe time you have enough statistically relevant data to rely on, themanager is likely nearing retirement.

At block 2004, process 2000 involves filtering each obtained result byrelevance. Conclusive answer application 102 determines a similaritybetween each search result of the search results and the seed sentence(e.g., as provided in block 2001). If a determined similarity is low,then conclusive answer application 102 compute a similarity for apreceding or consecutive sentence from the search results.

Relevance can be determined by using syntactic generalization.Conclusive answer application 102 can create a syntactic parse tree foreach obtained result. Each of these syntactic parse trees can becompared to a syntactic parse tree generated from the utterance (e.g.,at block 2001). The bag-of-words approach is extended towards extractingcommonalities between the syntactic parse trees of the seed sentence andthe sentence(s) obtained mined from another source (e.g., on the web).Syntactic generalization allows a domain-independent semantic measure oftopical similarity between a pair of sentences, without it combinationof sentences mined on the web would not form a meaningful text.

Common entities between seed and obtained sentence can be verified andan appropriateness metric obtained. The metric can include a syntacticgeneralization score (the cardinality of maximal common system ofsyntactic sub-trees). For two words of the same part of speech (POS),their generalization is the same word with the POS. If the lemmas forthe two words are different but the POS is the same, then the POSremains in the result. If lemmas are the same but POS is different,lemma stays in the result. A lemma represents a word without the relatedpart-of-speech information.

To illustrate this concept, consider an example of two natural languageexpressions. The meanings of the expressions are represented by logicformulas. The unification and anti-unification of these formulas areconstructed. Some words (entities) are mapped to predicates, some aremapped into their arguments, and some other words do not explicitlyoccur in logic form representation but indicate the above instantiationof predicates with arguments.

Consider the following two sentences “camera with digital zoom” and“camera with zoom for beginners.” To express the meanings, the followinglogic predicates are used:

camera(name_of_feature, type_of_users) and

zoom(type_of_zoom).

Note that this is a simplified example, and as such, may have a reducednumber of arguments as compared to more typical examples. Continuing theexample, the above expressions can be represented as:

camera(zoom(digital), AnyUser),

camera(zoom(AnyZoom), beginner)

According to the notation, variables (non-instantiated values, notspecified in NL expressions) are capitalized. Given the above pair offormulas, unification computes their most general specializationcamera(zoom(digital), beginner), and anti-unification computes theirmost specific generalization, camera(zoom(AnyZoom), AnyUser).

At the syntactic level, the expressions are subjected to ageneralization (‘{circumflex over ( )}’) of two noun phrases as:{NN-camera, PRP-with, [digital], NN-zoom [for beginners]}. Theexpressions in square brackets are eliminated because they occur in oneexpression but not occur in the other. As a result, obtain{NN-camera,PRP-with, NN-zoom]}, which is a syntactic analog of semanticgeneralization, is obtained.

The purpose of an abstract generalization is to find commonality betweenportions of text at various semantic levels. Generalization operationoccurs on the one or more levels. Examples of levels are paragraphlevel, sentence level, phrase level, and word level.

At each level (except word-level), individual words, the result ofgeneralization of two expressions is a set of expressions. In such set,for each pair of expressions so that one is less general than other, thelatter is eliminated. Generalization of two sets of expressions is a setof sets which are the results of pair-wise generalization of theseexpressions.

Only a single generalization exists for a pair of words: if words arethe same in the same form, the result is a node with this word in thisform. To involve word2vec models (Mikolov et al., 2015), computegeneralization of two different words, the following rule is used. Ifsubject1=subject2, then subject1{circumflex over ( )}subject2=<subject1,POS(subject1), 1>. Otherwise, if they have the same part-of-speech,subject1{circumflex over ( )}subject2=<*,POS(subject1),word2vecDistance(subject1{circumflex over ( )}subject2)>. Ifpart-of-speech is different, generalization is an empty tuple. It cannotbe further generalized.

For a pair of phrases, generalization includes all maximum ordered setsof generalization nodes for words in phrases so that the order of wordsis retained. In the following example,

“To buy digital camera today, on Monday.”

“Digital camera was a good buy today, first Monday of the month.”

Generalization is {<JJ-digital, NN-camera>,<NN-today, ADV,Monday>},where the generalization for noun phrases is followed by thegeneralization for an adverbial phrase. Verb buy is excluded from bothgeneralizations because it occurs in a different order in the abovephrases. Buy-digital-camera is not a generalization phrase because buyoccurs in different sequence with the other generalization nodes.

Therefore, a sentence similarity assessment can be expressed via ageneralization operator.A{circumflex over ( )}A1a=RST−Condition(VP( . . . ,NP rationalinvesting),*−retire)A{circumflex over ( )}A1b=NP rational investing),*−retire.

In the first search result A1a retire and rational investing areconnected in the similar way to the seed S: relational investing isconnected by the rhetorical relation Condition to the phrase includingretire. In A1b syntactic matching part is the same but these phrasesoccur in two different sentences and are related in a much more complexindirect way than in the seed. Hence A1a is a good fragment to includein the conclusive answer and A1b is not so good.

Returning to FIG. 20 , at block 2005, process 2000 involves filteringeach obtained result by a presence of argumentation. Conclusive answerapplication 102 can determine whether the obtained text is opinionatedor argumentative. Communicative discourse trees can be used. Forexample, by forming a communicative discourse tree for obtained text andproviding the obtained text to a trained machine learning model trainedto detect opinionated text, conclusive answer application 102 candetermine whether the text contains an opinion. The trained machinelearning model uses on mental states and/or communicative actionsidentified in the communicative discourse tree.

For example, conclusive answer application 102 can determine a presenceof argumentation in a sentence by forming a communicative discourse treefor the sentence and applying a trained machine learning model (e.g.,rhetoric agreement classifier 120) to the communicative discourse tree.The communicative discourse tree includes a root node. Examples arediscussed herein with respect to FIGS. 13 and 15 . Conclusive answerapplication 102 applies the trained machine learning model to thecommunicative discourse tree. The rhetoric agreement classifier 120 usesstandard machine learning techniques. For example, conclusive answerapplication 102 can train rhetoric agreement classifier 120 withpositive and negative classes communicative discourse trees or treepairs. The positive class includes request-response pairs from text thatincludes argumentation and the negative class includes pairs from textthat does not include argumentation. Examples of text with argumentationinclude product and service reviews.

At block 2006, process 2000 involves filtering each obtained result byappropriateness (or cohesiveness). Determining appropriateness can beaccomplished by applying grammar rules. For example, results thatinclude verbs in the imperative form should be excluded because suchresults are more likely to reflect advertisements or sales pitches.Selected fragments to be included into a conclusive answer shouldinclude opinionated sentences and avoid auxiliary comments, elaborationson topics which are not central to the topic of the seed and othersupplementary parts of a document. These roles of each sentence can bedetermined by CDT of each document paragraph.

At block 2007, process 2000 involves formatting the filtered textualcontent. In some cases, conclusive answer application 102 can reformatthe filtered content to better fit together, and joined in paragraphs.Once text fragments for a section are obtained, conclusive answerapplication 102 finds an optimal order to form the section text. Forexample, if both above text fragments are accepted (not just the firstone), the second should follow the first since it contains theconclusion . . . Hence . . . . And both these fragments are related tothe same main entity. Still, the resultant text would not read wellsince there is a strong deviation of topics towards finding an accountmanager, which is not the main topic of this section. Given an unorderedset of text fragments or paragraph, cohesiveness of the resultant textcannot be ensured. Instead, an optimal order for these fragments can befound, which minimizes the disturbance of content flow and coherence ofthe resultant text.

Process 2000 can be repeated for each utterance, according to thedetermined structure of the answer. For example, conclusive answerapplication 102 can iterate through each utterance. Conclusive answerapplication 102 can also combine sections in documents as appropriateand add reference sections for each section.

Modeling the Content Structure of Texts

Certain aspects can model a content structure of texts within a specificdomain in terms of the attributes of an entity this texts expresses andthe order in which these topics appear. Some research intended tocharacterize texts in terms of domain-independent rhetorical elements,such as schema items (McKeown, 1985) or rhetorical relations (Mann andThompson, 1988; Marcu, 1997). Conversely, (Barzilay and Lee 2004) focuson content, domain-dependent dimension of the structure of text. Theypresent an effective knowledge-lean method for learning content modelsfrom unannotated documents, utilizing a novel adaptation of algorithmsfor Hidden Markov Models. Certain aspects perform two complementarytasks: information ordering and extractive summarization. Theexperiments showed that incorporating content models in theseapplications gives a substantial improvement.

In general, the flow of text is determined by the topic change: howattributes of an entity evolve. (Barzilay and Lee 2004) designed a modelthat can specify, for example, that articles about mountains typicallycontain information about height, climate, assents, and climbers.Instead of manually determining the evolution of attributes (the topicsfor a given domain) a distributional view can be taken. It is possibleto machine learn these patterns of attribute evolution directly fromun-annotated texts via analysis of word distribution patterns. (Harris1982) wrote that a number of word recurrence patterns is correlated withvarious types of discourse structure type.

Advantages of a distributional perspective include both drasticreduction in human effort and recognition of “topics” that might notoccur to a human expert and yet, when explicitly modeled, aid inapplications. Of course, the success of the distributional approachdepends on the existence of recurrent patterns. In arbitrary documentcollections, such recurrent patterns might be too variable to be easilydetected by statistical means. However, research has shown that textsfrom the same domain tend to exhibit high similarity (Wray, 2002). Atthe same time, from the cognitive science perspective, this similarityis not random and is instead systematic, since text structurefacilitates a text comprehension by readers and their capability ofrecall (Bartlett, 1932).

We assume that text chunks convey information about a single attributeof an entity (a single topic). Specifying the length of text chunks candefines the granularity of the induced attribute/topic: we select theaverage paragraph length.

We will build a content model as a Hidden-Markov Model in which eachstate s corresponds to a distinct topic and generates sentences relevantto that topic according to a state-specific language model p_(s). Notethat standard-gram language models can therefore be considered to bedegenerate (single-state) content models. State transition probabilitiesgive the probability of changing from a given topic to another, therebycapturing constraints attribute evolution (topic shift).

In our implementation, we use bigram language models, so that theprobability of an −word sentence x=w₁ w₂ . . . w_(n) being generated bya state sp _(s)(x)=Π_(i=1) ^(n) p _(s)(w _(i) |w _(i-1)).

We will now describe state bigram probabilities p_(s) (w₁|w_(i-1)). Toinitialize a set of attributes by partitioning all of the paragraphs (ortext chunks) from the documents in a given domain-specific collectioninto clusters. First, we create clusters via complete-link clustering,measuring sentence similarity by the cosine metric using word bigrams asfeatures. Then, given our knowledge that documents may sometimes discussnew and/or irrelevant content as well, we create an AUX cluster bymerging together all clusters containing #paragraphs <t (selectedthreshold). We rely on the assumption that such clusters consist of“outlier” sentences.

Given a set=c₁, c₂, . . . , c_(m) of m clusters, where c_(m) is the AUXcluster, we construct a content model with corresponding states s₁, s₂,. . . , s_(m). we refer to s_(m) as the insertion state.

For each state s_(i) i<m bigram probabilities (which induce the state'ssentence-emission probabilities) are estimated using smoothed countsfrom the corresponding cluster

${{p_{s_{i}}\left( {w^{\prime}{❘w}} \right)}\overset{def}{=}\frac{{f_{c_{i}}\left( {ww}^{\prime} \right)} + \delta_{1}}{{f_{c_{i}}(w)} + {\delta_{1}{❘V❘}}}},$where f c₁(y) is the frequency with which word sequence y occurs withinthe sentences in cluster c_(i), and Vis the vocabulary.

We want the insertion state s_(m) to simulate digressions or unseenattributes. We ignore the content of AUX cluster and force the languagemodel to be complementary to those of the other states by setting

${p_{s_{m}}\left( {w^{\prime}{❘w}} \right)}\overset{def}{=}{\frac{1 - {\max_{{i:i} < m}{p_{s_{i}}\left( {w^{\prime}{❘w}} \right)}}}{\sum_{u \in V}\left( {1 - {\max_{{i:i} < m}{p_{s_{i}}\left( {u{❘w}} \right)}}} \right)}.}$

Our state-transition probability estimates arise from considering howthe paragraphs from the same document are distributed across theclusters. For two clusters c and c′ we define D(c, c′) as the number ofdocuments in which a paragraph from c immediately precedes one from c′.D(c) is the number of documents containing paragraphs from c. For anytwo states s_(i) and s_(j), i,j<m, we rely on the following smoothestimate of the probability of transitioning from s_(i) to s_(j):

${p\left( {s_{j}{❘s_{i}}} \right)} = {\frac{{D\left( {c_{i},c_{j}} \right)} + \delta_{2}}{{D\left( c_{i} \right)} + {\delta_{2}m}}.}$Building Answer Document Based on Similarity and Compositional Semantics

Certain aspects can model a document in a vector representation using aparagraph vector model (Le and Mikolov 2014) that computes continuousdistributed vector representations of varying-length texts. The sourcedocuments' section that are semantically close (or similar) to thedesired document is identified in this vector space using cosinesimilarity. The structure of similar articles cab then be emulated, theimportant sections identified and assign relevant web-content orintranet content assigned to the sections.

The entire Wikipedia to obtain D-dimensional representations ofwords/entities as well as documents using the paragraph vectordistributed memory model (Le and Mikolov, 2014). Similar articles areidentified using cosine similarity between the vector representations ofthe missing entity and representations of the existing entities(entities that have corresponding articles). Content from the similararticles are used to train multi-class classifiers that can assignweb-retrieved content on the red-linked entity to relevant sections ofthe article. The paragraph vector distributed memory model is used toidentify similar documents to rely upon on one hand and also aninference of vector representations of new paragraphs retrieved from theweb on the other hand.

We take a sequence of words from a similar document and approach thelast word that can be reused. Then we attempt to predict the next wordusing PV-DM. The PV-DM model is based on the principle that severalcontexts sampled from the paragraph can be used to predict the nextword. Given a sequence of T words (w₁, w₂, . . . , w_(T)), the task isto maximize the average log probability. In the top equation, c is thesize of the context (number of words before and after the current wordto be used for training). The conditional probability of w_(t+j) givenw_(t) is given by the softmax function (Bridle 1990) in bottom equation,where v_(wt+j) and v_(w) refers to the output and the input vectorrepresentations of the word w, respectively. W refers to the totalnumber of words in the vocabulary.

$F = {\frac{1}{T}{\sum\limits_{t = 1}^{t = T}{\sum\limits_{{{- c} \leq j \leq c},{j \neq 0}}{\log{p\left( {w_{t + j}{❘w_{t}}} \right)}}}}}$${p\left( {w_{t + j}{❘w_{t}}} \right)} = \frac{\exp\left( {\upsilon_{w_{t + j}}^{\prime}{\,^{T}\upsilon_{w_{t}}}} \right)}{\sum\limits_{w = 1}^{W}{\exp\left( {\upsilon_{w}^{\prime}{\,^{T}\upsilon_{w_{t}}}} \right)}}$Exemplary Computing Systems

FIG. 21 depicts a simplified diagram of a distributed system 2100 forimplementing one of the aspects. In the illustrated aspect, distributedsystem 2100 includes one or more client computing devices 2102, 2104,2106, and 2108, which are configured to execute and operate a clientapplication such as a web browser, proprietary client (e.g., OracleForms), or the like over one or more network(s) 2110. Server 2112 may becommunicatively coupled with remote client computing devices 2102, 2104,2106, and 2108 via network 2110.

In various aspects, server 2112 may be adapted to run one or moreservices or software applications provided by one or more of thecomponents of the system. The services or software applications caninclude nonvirtual and virtual environments. Virtual environments caninclude those used for virtual events, tradeshows, simulators,classrooms, shopping exchanges, and enterprises, whether two- orthree-dimensional (3D) representations, page-based logical environments,or otherwise. In some aspects, these services may be offered asweb-based or cloud services or under a Software as a Service (SaaS)model to the users of client computing devices 2102, 2104, 2106, and/or2108. Users operating client computing devices 2102, 2104, 2106, and/or2108 may in turn utilize one or more client applications to interactwith server 2112 to utilize the services provided by these components.

In the configuration depicted in the figure, the software components2118, 2120 and 2122 of system 2100 are shown as being implemented onserver 2112. In other aspects, one or more of the components of system2100 and/or the services provided by these components may also beimplemented by one or more of the client computing devices 2102, 2104,2106, and/or 2108. Users operating the client computing devices may thenutilize one or more client applications to use the services provided bythese components. These components may be implemented in hardware,firmware, software, or combinations thereof. It should be appreciatedthat various different system configurations are possible, which may bedifferent from distributed system 2100. The aspect shown in the figureis thus one example of a distributed system for implementing an aspectsystem and is not intended to be limiting.

Client computing devices 2102, 2104, 2106, and/or 2108 may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 10, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS),Blackberry®, or other communication protocol enabled. The clientcomputing devices can be general purpose personal computers including,by way of example, personal computers and/or laptop computers runningvarious versions of Microsoft Windows®, Apple Macintosh®, and/or Linuxoperating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices 2102, 2104,2106, and 2108 may be any other electronic device, such as a thin-clientcomputer, an Internet-enabled gaming system (e.g., a Microsoft Xboxgaming console with or without a Kinect® gesture input device), and/or apersonal messaging device, capable of communicating over network(s)2110.

Although exemplary distributed system 2100 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 2112.

Network(s) 2110 in distributed system 2100 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including without limitation TCP/IP (transmission controlprotocol/Internet protocol), SNA (systems network architecture), IPX(Internet packet exchange), AppleTalk, and the like. Merely by way ofexample, network(s) 2110 can be a local area network (LAN), such as onebased on Ethernet, Token-Ring and/or the like. Network(s) 2110 can be awide-area network and the Internet. It can include a virtual network,including without limitation a virtual private network (VPN), anintranet, an extranet, a public switched telephone network (PSTN), aninfra-red network, a wireless network (e.g., a network operating underany of the Institute of Electrical and Electronics (IEEE) 802.21 suiteof protocols, Bluetooth®, and/or any other wireless protocol); and/orany combination of these and/or other networks.

Server 2112 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. Server 2112 caninclude one or more virtual machines running virtual operating systems,or other computing architectures involving virtualization. One or moreflexible pools of logical storage devices can be virtualized to maintainvirtual storage devices for the server. Virtual networks can becontrolled by server 2112 using software defined networking. In variousaspects, server 2112 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 2112 may correspond to a server for performingprocessing described above according to an aspect of the presentdisclosure.

Server 2112 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 2112 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include without limitation thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 2112 may include one or moreapplications to analyze and consolidate data feeds and/or event updatesreceived from users of client computing devices 2102, 2104, 2106, and2108. As an example, data feeds and/or event updates may include, butare not limited to, Twitter® feeds, Facebook® updates or real-timeupdates received from one or more third party information sources andcontinuous data streams, which may include real-time events related tosensor data applications, financial tickers, network performancemeasuring tools (e.g., network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like. Server 2112 may also include one or moreapplications to display the data feeds and/or real-time events via oneor more display devices of client computing devices 2102, 2104, 2106,and 2108.

Distributed system 2100 may also include one or more databases 2114 and2116.

Databases 2114 and 2116 may reside in a variety of locations. By way ofexample, one or more of databases 2114 and 2116 may reside on anon-transitory storage medium local to (and/or resident in) server 2112.Alternatively, databases 2114 and 2116 may be remote from server 2112and in communication with server 2112 via a network-based or dedicatedconnection. In one set of aspects, databases 2114 and 2116 may reside ina storage-area network (SAN). Similarly, any necessary files forperforming the functions attributed to server 2112 may be stored locallyon server 2112 and/or remotely, as appropriate. In one set of aspects,databases 2114 and 2116 may include relational databases, such asdatabases provided by Oracle, that are adapted to store, update, andretrieve data in response to SQL-formatted commands.

FIG. 22 is a simplified block diagram of one or more components of asystem environment 2200 by which services provided by one or morecomponents of an aspect system may be offered as cloud services, inaccordance with an aspect of the present disclosure. In the illustratedaspect, system environment 2200 includes one or more client computingdevices 2204, 2206, and 2208 that may be used by users to interact witha cloud infrastructure system 2202 that provides cloud services. Theclient computing devices may be configured to operate a clientapplication such as a web browser, a proprietary client application(e.g., Oracle Forms), or some other application, which may be used by auser of the client computing device to interact with cloudinfrastructure system 2202 to use services provided by cloudinfrastructure system 2202.

It should be appreciated that cloud infrastructure system 2202 depictedin the figure may have other components than those depicted. Further,the aspect shown in the figure is only one example of a cloudinfrastructure system that may incorporate an aspect of the invention.In some other aspects, cloud infrastructure system 2202 may have more orfewer components than shown in the figure, may combine two or morecomponents, or may have a different configuration or arrangement ofcomponents.

Client computing devices 2204, 2206, and 2208 may be devices similar tothose described above for 2102, 2104, 2106, and 2108.

Although exemplary system environment 2200 is shown with three clientcomputing devices, any number of client computing devices may besupported. Other devices such as devices with sensors, etc. may interactwith cloud infrastructure system 2202.

Network(s) 2210 may facilitate communications and exchange of databetween clients 2204, 2206, and 2208 and cloud infrastructure system2202. Each network may be any type of network familiar to those skilledin the art that can support data communications using any of a varietyof commercially-available protocols, including those described above fornetwork(s) 2110.

Cloud infrastructure system 2202 may comprise one or more computersand/or servers that may include those described above for server 2122.

In certain aspects, services provided by the cloud infrastructure systemmay include a host of services that are made available to users of thecloud infrastructure system on demand, such as online data storage andbackup solutions, Web-based e-mail services, hosted office suites anddocument collaboration services, database processing, managed technicalsupport services, and the like. Services provided by the cloudinfrastructure system can dynamically scale to meet the needs of itsusers. A specific instantiation of a service provided by cloudinfrastructure system is referred to herein as a “service instance.” Ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a cloud service provider's system isreferred to as a “cloud service.” Typically, in a public cloudenvironment, servers and systems that make up the cloud serviceprovider's system are different from the customer's own on-premisesservers and systems. For example, a cloud service provider's system mayhost an application, and a user may, via a communication network such asthe Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain aspects, cloud infrastructure system 2202 may include a suiteof applications, middleware, and database service offerings that aredelivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

Large volumes of data, sometimes referred to as big data, can be hostedand/or manipulated by the infrastructure system on many levels and atdifferent scales. Such data can include data sets that are so large andcomplex that it can be difficult to process using typical databasemanagement tools or traditional data processing applications. Forexample, terabytes of data may be difficult to store, retrieve, andprocess using personal computers or their rack-based counterparts. Suchsizes of data can be difficult to work with using most currentrelational database management systems and desktop statistics andvisualization packages. They can require massively parallel processingsoftware running thousands of server computers, beyond the structure ofcommonly used software tools, to capture, curate, manage, and processthe data within a tolerable elapsed time.

Extremely large data sets can be stored and manipulated by analysts andresearchers to visualize large amounts of data, detect trends, and/orotherwise interact with the data. Tens, hundreds, or thousands ofprocessors linked in parallel can act upon such data in order to presentit or simulate external forces on the data or what it represents. Thesedata sets can involve structured data, such as that organized in adatabase or otherwise according to a structured model, and/orunstructured data (e.g., emails, images, data blobs (binary largeobjects), web pages, complex event processing). By leveraging an abilityof an aspect to relatively quickly focus more (or fewer) computingresources upon an objective, the cloud infrastructure system may bebetter available to carry out tasks on large data sets based on demandfrom a business, government agency, research organization, privateindividual, group of like-minded individuals or organizations, or otherentity.

In various aspects, cloud infrastructure system 2202 may be adapted toautomatically provision, manage and track a customer's subscription toservices offered by cloud infrastructure system 2202. Cloudinfrastructure system 2202 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 2202 is owned by anorganization selling cloud services (e.g., owned by Oracle) and theservices are made available to the general public or different industryenterprises. As another example, services may be provided under aprivate cloud model in which cloud infrastructure system 2202 isoperated solely for a single organization and may provide services forone or more entities within the organization. The cloud services mayalso be provided under a community cloud model in which cloudinfrastructure system 2202 and the services provided by cloudinfrastructure system 2202 are shared by several organizations in arelated community. The cloud services may also be provided under ahybrid cloud model, which is a combination of two or more differentmodels.

In some aspects, the services provided by cloud infrastructure system2202 may include one or more services provided under Software as aService (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 2202. Cloud infrastructure system 2202 then performs processingto provide the services in the customer's subscription order.

In some aspects, the services provided by cloud infrastructure system2202 may include, without limitation, application services, platformservices and infrastructure services. In some examples, applicationservices may be provided by the cloud infrastructure system via a SaaSplatform. The SaaS platform may be configured to provide cloud servicesthat fall under the SaaS category. For example, the SaaS platform mayprovide capabilities to build and deliver a suite of on-demandapplications on an integrated development and deployment platform. TheSaaS platform may manage and control the underlying software andinfrastructure for providing the SaaS services. By utilizing theservices provided by the SaaS platform, customers can utilizeapplications executing on the cloud infrastructure system. Customers canacquire the application services without the need for customers topurchase separate licenses and support. Various different SaaS servicesmay be provided. Examples include, without limitation, services thatprovide solutions for sales performance management, enterpriseintegration, and business flexibility for large organizations.

In some aspects, platform services may be provided by the cloudinfrastructure system via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include without limitation servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by the cloud infrastructuresystem without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someaspects, platform services provided by the cloud infrastructure systemmay include database cloud services, middleware cloud services (e.g.,Oracle Fusion Middleware services), and Java cloud services. In oneaspect, database cloud services may support shared service deploymentmodels that enable organizations to pool database resources and offercustomers a Database as a Service in the form of a database cloud.Middleware cloud services may provide a platform for customers todevelop and deploy various business applications, and Java cloudservices may provide a platform for customers to deploy Javaapplications, in the cloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain aspects, cloud infrastructure system 2202 may also includeinfrastructure resources 2230 for providing the resources used toprovide various services to customers of the cloud infrastructuresystem. In one aspect, infrastructure resources 2230 may includepre-integrated and optimized combinations of hardware, such as servers,storage, and networking resources to execute the services provided bythe PaaS platform and the SaaS platform.

In some aspects, resources in cloud infrastructure system 2202 may beshared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 2202 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain aspects, a number of internal shared services 2232 may beprovided that are shared by different components or modules of cloudinfrastructure system 2202 and by the services provided by cloudinfrastructure system 2202. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain aspects, cloud infrastructure system 2202 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one aspect, cloudmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by cloudinfrastructure system 2202, and the like.

In one aspect, as depicted in the figure, cloud management functionalitymay be provided by one or more modules, such as an order managementmodule 2220, an order orchestration module 2222, an order provisioningmodule 2224, an order management and monitoring module 2226, and anidentity management module 2228. These modules may include or beprovided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In exemplary operation 2234, a customer using a client device, such asclient device 2204, 2206 or 2208, may interact with cloud infrastructuresystem 2202 by requesting one or more services provided by cloudinfrastructure system 2202 and placing an order for a subscription forone or more services offered by cloud infrastructure system 2202. Incertain aspects, the customer may access a cloud User Interface (UI),cloud UI 2222, cloud UI 2214 and/or cloud UI 2216 and place asubscription order via these UIs. The order information received bycloud infrastructure system 2202 in response to the customer placing anorder may include information identifying the customer and one or moreservices offered by the cloud infrastructure system 2202 that thecustomer intends to subscribe to.

After an order has been placed by the customer, the order information isreceived via the cloud UIs, 2222, 2214 and/or 2216.

At operation 2236, the order is stored in order database 2218. Orderdatabase 2218 can be one of several databases operated by cloudinfrastructure system 2202 and operated in conjunction with other systemelements.

At operation 2238, the order information is forwarded to an ordermanagement module 2220. In some instances, order management module 2220may be configured to perform billing and accounting functions related tothe order, such as verifying the order, and upon verification, bookingthe order.

At operation 2240, information regarding the order is communicated to anorder orchestration module 2222. Order orchestration module 2222 mayutilize the order information to orchestrate the provisioning ofservices and resources for the order placed by the customer. In someinstances, order orchestration module 2222 may orchestrate theprovisioning of resources to support the subscribed services using theservices of order provisioning module 2224.

In certain aspects, order orchestration module 2222 enables themanagement of business processes associated with each order and appliesbusiness logic to determine whether an order should proceed toprovisioning. At operation 2242, upon receiving an order for a newsubscription, order orchestration module 2222 sends a request to orderprovisioning module 2224 to allocate resources and configure thoseresources needed to fulfill the subscription order. Order provisioningmodule 2224 enables the allocation of resources for the services orderedby the customer. Order provisioning module 2224 provides a level ofabstraction between the cloud services provided by cloud infrastructuresystem 2200 and the physical implementation layer that is used toprovision the resources for providing the requested services. Orderorchestration module 2222 may thus be isolated from implementationdetails, such as whether or not services and resources are actuallyprovisioned on the fly or pre-provisioned and only allocated/assignedupon request.

At operation 2244, once the services and resources are provisioned, anotification of the provided service may be sent to customers on clientdevices 2204, 2206 and/or 2208 by order provisioning module 2224 ofcloud infrastructure system 2202.

At operation 2246, the customer's subscription order may be managed andtracked by an order management and monitoring module 2226. In someinstances, order management and monitoring module 2226 may be configuredto collect usage statistics for the services in the subscription order,such as the amount of storage used, the amount data transferred, thenumber of users, and the amount of system up time and system down time.

In certain aspects, cloud infrastructure system 2200 may include anidentity management module 2228. Identity management module 2228 may beconfigured to provide identity services, such as access management andauthorization services in cloud infrastructure system 2200. In someaspects, identity management module 2228 may control information aboutcustomers who wish to utilize the services provided by cloudinfrastructure system 2202. Such information can include informationthat authenticates the identities of such customers and information thatdescribes which actions those customers are authorized to performrelative to various system resources (e.g., files, directories,applications, communication ports, memory segments, etc.) Identitymanagement module 2228 may also include the management of descriptiveinformation about each customer and about how and by whom thatdescriptive information can be accessed and modified.

FIG. 23 illustrates an exemplary computer system 2300, in which variousaspects of the present invention may be implemented. The system 2300 maybe used to implement any of the computer systems described above. Asshown in the figure, computer system 2300 includes a processing unit2304 that communicates with a number of peripheral subsystems via a bussubsystem 2302. These peripheral subsystems may include a processingacceleration unit 2306, an I/O subsystem 2308, a storage subsystem 2318and a communications subsystem 2324. Storage subsystem 2318 includestangible computer-readable storage media 2322 and a system memory 2310.

Bus subsystem 2302 provides a mechanism for letting the variouscomponents and subsystems of computer system 2300 communicate with eachother as intended. Although bus subsystem 2302 is shown schematically asa single bus, alternative aspects of the bus subsystem may utilizemultiple buses. Bus subsystem 2302 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P2386.1standard.

Processing unit 2304, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 2300. One or more processorsmay be included in processing unit 2304. These processors may includesingle core or multicore processors. In certain aspects, processing unit2304 may be implemented as one or more independent processing units 2332and/or 2334 with single or multicore processors included in eachprocessing unit. In other aspects, processing unit 2304 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various aspects, processing unit 2304 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processor(s)2304 and/or in storage subsystem 2318. Through suitable programming,processor(s) 2304 can provide various functionalities described above.Computer system 2300 may additionally include a processing accelerationunit 2306, which can include a digital signal processor (DSP), aspecial-purpose processor, and/or the like.

I/O subsystem 2308 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system2300 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 2300 may comprise a storage subsystem 2318 thatcomprises software elements, shown as being currently located within asystem memory 2310. System memory 2310 may store program instructionsthat are loadable and executable on processing unit 2304, as well asdata generated during the execution of these programs.

Depending on the configuration and type of computer system 2300, systemmemory 2310 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 2304. In some implementations, system memory 2310 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system2300, such as during start-up, may typically be stored in the ROM. Byway of example, and not limitation, system memory 2310 also illustratesapplication programs 2312, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 2314, and an operating system 2316. By wayof example, operating system 2316 may include various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems, avariety of commercially-available UNIX® or UNIX-like operating systems(including without limitation the variety of GNU/Linux operatingsystems, the Google Chrome® OS, and the like) and/or mobile operatingsystems such as iOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, andPalm® OS operating systems.

Storage subsystem 2318 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some aspects. Software (programs, codemodules, instructions) that when executed by a processor provide thefunctionality described above may be stored in storage subsystem 2318.These software modules or instructions may be executed by processingunit 2304. Storage subsystem 2318 may also provide a repository forstoring data used in accordance with the present invention.

Storage subsystem 2318 may also include a computer-readable storagemedia reader 2320 that can further be connected to computer-readablestorage media 2322. Together and, optionally, in combination with systemmemory 2310, computer-readable storage media 2322 may comprehensivelyrepresent remote, local, fixed, and/or removable storage devices plusstorage media for temporarily and/or more permanently containing,storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 2322 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible, non-transitorycomputer-readable storage media such as RAM, ROM, electronicallyerasable programmable ROM (EEPROM), flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD), or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible computer readablemedia. When specified, this can also include nontangible, transitorycomputer-readable media, such as data signals, data transmissions, orany other medium which can be used to transmit the desired informationand which can be accessed by computing system 2300.

By way of example, computer-readable storage media 2322 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 2322 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 2322 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 2300.

Communications subsystem 2324 provides an interface to other computersystems and networks. Communications subsystem 2324 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 2300. For example, communications subsystem 2324may enable computer system 2300 to connect to one or more devices viathe Internet. In some aspects, communications subsystem 2324 can includeradio frequency (RF) transceiver components for accessing wireless voiceand/or data networks (e.g., using cellular telephone technology,advanced data network technology, such as 3G, 4G or EDGE (enhanced datarates for global evolution), WiFi (IEEE 802.21 family standards, orother mobile communication technologies, or any combination thereof),global positioning system (GPS) receiver components, and/or othercomponents. In some aspects, communications subsystem 2324 can providewired network connectivity (e.g., Ethernet) in addition to or instead ofa wireless interface.

In some aspects, communications subsystem 2324 may also receive inputcommunication in the form of structured and/or unstructured data feeds2326, event streams 2328, event updates 2323, and the like on behalf ofone or more users who may use computer system 2300.

By way of example, communications subsystem 2324 may be configured toreceive unstructured data feeds 2326 in real-time from users of socialmedia networks and/or other communication services such as Twitter®feeds, Facebook® updates, web feeds such as Rich Site Summary (RSS)feeds, and/or real-time updates from one or more third party informationsources.

Additionally, communications subsystem 2324 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 2328 of real-time events and/or event updates 2323, thatmay be continuous or unbounded in nature with no explicit end. Examplesof applications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 2324 may also be configured to output thestructured and/or unstructured data feeds 2326, event streams 2328,event updates 2323, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 2300.

Computer system 2300 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 2300 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious aspects.

In the foregoing specification, aspects of the invention are describedwith reference to specific aspects thereof, but those skilled in the artwill recognize that the invention is not limited thereto. Variousfeatures and aspects of the above-described invention may be usedindividually or jointly. Further, aspects can be utilized in any numberof environments and applications beyond those described herein withoutdeparting from the broader spirit and scope of the specification. Thespecification and drawings are, accordingly, to be regarded asillustrative rather than restrictive.

What is claimed is:
 1. A method of computationally fortifying an answerusing syntactic parse trees, the method comprising: accessing a seedsentence comprising a first plurality of text fragments; obtaining asearch result by providing, to a search engine, at least one of thefirst plurality of text fragments, wherein the search result comprises asecond plurality of text fragments; generating, from the search result,a communicative discourse tree, wherein generating the communicativediscourse tree comprises: creating a discourse tree from the secondplurality of text fragments, wherein the discourse tree comprises aplurality of nodes, each nonterminal node representing a rhetoricalrelationship between two fragments of the second plurality of textfragments and each terminal node of the nodes of the discourse tree isassociated with one of the fragments of the second plurality of textfragments; and matching each fragment that has a verb to a verbsignature; determining whether the search result contains argumentationby providing the communicative discourse tree to a machine learningmodel trained to detect text comprising argumentation; responsive todetermining that the search result contains argumentation, constructinga paragraph from the search result; and providing the paragraph to auser device.
 2. The method of claim 1, further comprising training themachine learning model by: accessing a set of training data comprising atraining pair, the training pair comprising a first communicativediscourse tree that represents text comprising argumentation and asecond communicative discourse tree that represents text withoutargumentation; and providing one of the training pairs to the machinelearning model; receiving, from the machine learning model, a determinedpresence of argumentation; calculating a loss function by calculating adifference between the determined presence of argumentation and anexpected presence of argumentation; and adjusting internal parameters ofthe machine learning model to minimize the loss function.
 3. The methodof claim 1, wherein obtaining a search result comprises: identifying anentity within the first plurality of text fragments; and providing theentity to the search engine.
 4. The method of claim 3, whereinidentifying an entity further comprises: constructing a first syntacticparse tree from the first plurality of text fragments; and identifyingthe entity within the first syntactic parse tree.
 5. The method of claim4, wherein identifying a noun phrase comprises extracting the nounphrase from a node of the first syntactic parse tree.
 6. The method ofclaim 1, further comprising identifying a relevancy metric based on acommon entity between a first syntactic parse tree and a secondsyntactic parse tree, and wherein providing the paragraph to the userdevice is based on a determination that the relevancy metric is greaterthan a threshold.
 7. The method of claim 6, wherein computing therelevancy metric comprises applying an additional a machine learningmodel to the first syntactic parse tree and the second syntactic parsetree.
 8. A system comprising: a non-transitory computer-readable mediumstoring computer-executable program instructions; and a processingdevice communicatively coupled to the non-transitory computer-readablemedium for executing the computer-executable program instructions,wherein executing the computer-executable program instructionsconfigures the processing device to perform operations comprising:accessing a seed sentence comprising a first plurality of textfragments; obtaining a search result by providing, to a search engine,at least one of the first plurality of text fragments, wherein thesearch result comprises a second plurality of text fragments;generating, from the search result, a communicative discourse tree,wherein generating the communicative discourse tree comprises: creatinga discourse tree from the second plurality of text fragments, whereinthe discourse tree comprises a plurality of nodes, each nonterminal noderepresenting a rhetorical relationship between two fragments of thesecond plurality of text fragments and each terminal node of the nodesof the discourse tree is associated with one of the fragments of thesecond plurality of text fragments; and matching each fragment that hasa verb to a verb signature; determining whether the search resultcontains argumentation by providing the communicative discourse tree toa machine learning model trained to detect text comprisingargumentation; responsive to determining that the search result containsargumentation, constructing a paragraph from the search result; andproviding the paragraph to a user device.
 9. The system of claim 8,wherein executing the computer-executable program instructionsconfigures the processing device to perform operations comprisingtraining the machine learning model by: accessing a set of training datacomprising a training pair, the training pair comprising a firstcommunicative discourse tree that represents text comprisingargumentation and a second communicative discourse tree that representstext without argumentation; and providing one of the training pairs tothe machine learning model; receiving, from the machine learning model,a determined presence of argumentation; calculating a loss function bycalculating a difference between the determined presence ofargumentation and an expected presence of argumentation; and adjustinginternal parameters of the machine learning model to minimize the lossfunction.
 10. The system of claim 8, wherein obtaining a search resultcomprises: identifying an entity within the first plurality of textfragments; and providing the entity to the search engine.
 11. The systemof claim 10, wherein identifying an entity further comprises:constructing a first syntactic parse tree from the first plurality oftext fragments; and identifying the entity within the syntactic parsetree.
 12. The system of claim 11, wherein identifying a noun phrasecomprises extracting the noun phrase from a node of the first syntacticparse tree.
 13. The system of claim 8, wherein executing thecomputer-executable program instructions configures the processingdevice to perform operations comprising: identifying a relevancy metricbased on a common entity between a first syntactic parse tree and asecond syntactic parse tree and wherein providing the paragraph to theuser device is based on a determination that the relevancy metric isgreater than a threshold.
 14. The system of claim 13, wherein computingthe relevancy metric comprises applying an additional a machine learningmodel to the first syntactic parse tree and the second syntactic parsetree.
 15. A non-transitory computer-readable storage medium storingcomputer-executable program instructions, wherein when executed by aprocessing device, the computer-executable program instructions causethe processing device to perform operations comprising: accessing a seedsentence comprising a first plurality of text fragments; obtaining asearch result by providing, to a search engine, at least one of thefirst plurality of text fragments, wherein the search result comprises asecond plurality of text fragments; generating, from the search result,a communicative discourse tree, wherein generating the communicativediscourse tree comprises: creating a discourse tree from the secondplurality of text fragments, wherein the discourse tree comprises aplurality of nodes, each nonterminal node representing a rhetoricalrelationship between two fragments of the second plurality of textfragments and each terminal node of the nodes of the discourse tree isassociated with one of the fragments of the second plurality of textfragments; and matching each fragment that has a verb to a verbsignature; determining whether the search result contains argumentationby providing the communicative discourse tree to a machine learningmodel trained to detect text comprising argumentation; responsive todetermining that the search result contains argumentation, constructinga paragraph from the search result; and providing the paragraph to auser device.
 16. The storage medium of claim 15, wherein when executedby a processing device, the computer-executable program instructionscause the processing device to perform operations comprising trainingthe machine learning model by: accessing a set of training datacomprising a training pair, the training pair comprising a firstcommunicative discourse tree that represents text comprisingargumentation and a second communicative discourse tree that representstext without argumentation; and providing one of the training pairs tothe machine learning model; receiving, from the machine learning model,a determined presence of argumentation; calculating a loss function bycalculating a difference between the determined presence ofargumentation and an expected presence of argumentation; and adjustinginternal parameters of the machine learning model to minimize the lossfunction.
 17. The storage medium of claim 15, wherein obtaining a searchresult comprises: identifying an entity within the first plurality oftext fragments; and providing the entity to the search engine.
 18. Thestorage medium of claim 17, wherein identifying an entity furthercomprises: constructing a syntactic parse tree from the first pluralityof text fragments; and identifying the entity within the first syntacticparse tree.
 19. The storage medium of claim 18, wherein identifying anoun phrase comprises extracting the noun phrase from a node of thefirst syntactic parse tree.
 20. The storage medium of claim 15, whereinwhen executed by a processing device, the computer-executable programinstructions cause the processing device to perform operationscomprising: identifying a relevancy metric based on a common entitybetween a first syntactic parse tree and a second syntactic parse treeand wherein providing the paragraph to the user device is based on adetermination that the relevancy metric is greater than a threshold.